{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1424a3fd",
   "metadata": {},
   "source": [
    "# Personalized Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ad52b6",
   "metadata": {},
   "source": [
    "**NOTE**: This notebook depends upon the the Retrotech dataset. If you have any issues, please rerun the [Setting up the Retrotech Dataset](../ch4/1.ch4-setting-up-the-retrotech-dataset.ipynb) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f49e97a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../..')\n",
    "from aips import *\n",
    "import os\n",
    "from IPython.display import display,HTML\n",
    "from pyspark.sql import SparkSession\n",
    "#spark = SparkSession.builder.appName(\"aips-ch9\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65b1073e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Recommended for making ALS run faster, if you have enough memory / cores allocated to docker\n",
    "from pyspark.conf import SparkConf\n",
    "conf = SparkConf()\n",
    "conf.set(\"spark.driver.memory\", '11g')\n",
    "conf.set(\"spark.executor.cores\", \"6\")\n",
    "conf.set(\"spark.dynamicAllocation.enabled\", \"true\")\n",
    "conf.set(\"spark.executor.memory\", \"11g\")\n",
    "conf.set(\"spark.dynamicAllocation.enabled\", \"true\")\n",
    "conf.set(\"spark.dynamicAllocation.executorMemoryOverhead\", \"11g\")\n",
    "conf.set(\"spark.driver.memory\", \"11g\")\n",
    "spark = SparkSession.builder.appName(\"aips-ch10\").config(conf=conf).getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48197bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Load product data\n",
    "from pyspark.sql.types import LongType, StringType, StructField, StructType, BooleanType, ArrayType, IntegerType\n",
    "products_collection = \"products\"    \n",
    "product_update_opts={\"zkhost\": \"aips-zk\", \"collection\": products_collection}\n",
    "spark.read.format(\"solr\").options(**product_update_opts).load().createOrReplaceTempView(\"products\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d1f3344",
   "metadata": {},
   "source": [
    "# Collaborative Filtering with Implicit Preferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "845c2f03",
   "metadata": {},
   "outputs": [],
   "source": [
    "def aggregate_signals(signals_collection, signals_aggregation_collection, signals_aggregation_query):\n",
    "\n",
    "    create_collection(signals_aggregation_collection)\n",
    "\n",
    "    print(\"Aggregating Signals to Create Signals Boosts...\")\n",
    "    signals_opts={\"zkhost\": \"aips-zk\", \"collection\": signals_collection}\n",
    "    signals_boosting_opts={\"zkhost\": \"aips-zk\", \"collection\": signals_aggregation_collection, \"gen_uniq_key\": \"true\", \"commit_within\": \"5000\"}\n",
    "    df = spark.read.format(\"solr\").options(**signals_opts).load()\n",
    "    df.registerTempTable(\"signals\")\n",
    "\n",
    "\n",
    "    spark.sql(signals_aggregation_query).write.format(\"solr\").options(**signals_boosting_opts).mode(\"overwrite\").save()\n",
    "    print(\"Signals Aggregation Completed!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "721bec00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wiping 'user_product_implicit_preferences' collection\n",
      "[('action', 'CREATE'), ('name', 'user_product_implicit_preferences'), ('numShards', 1), ('replicationFactor', 1)]\n",
      "Creating 'user_product_implicit_preferences' collection\n",
      "Status: Success\n",
      "Aggregating Signals to Create Signals Boosts...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/spark/python/pyspark/sql/dataframe.py:229: FutureWarning: Deprecated in 2.0, use createOrReplaceTempView instead.\n",
      "  warnings.warn(\"Deprecated in 2.0, use createOrReplaceTempView instead.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Signals Aggregation Completed!\n"
     ]
    }
   ],
   "source": [
    "#Weighting multiple signal types by user\n",
    "\n",
    "signals_collection=\"signals\"\n",
    "signals_aggregation_collection=\"user_product_implicit_preferences\"\n",
    "\n",
    "mixed_signal_types_aggregation = \"\"\"\n",
    "select user, product, (\n",
    "      (1 * click_boost) \n",
    "    + (0 * add_to_cart_boost) \n",
    "    + (0 * purchase_boost) ) as rating\n",
    "from (\n",
    "  select user, product, \n",
    "    sum(click) as click_boost,\n",
    "    sum(add_to_cart) as add_to_cart_boost,\n",
    "    sum(purchase) as purchase_boost\n",
    "  from (  \n",
    "      select cap.user, cap.target as product, \n",
    "        if(cap.type = 'click', 1, 0) as click, \n",
    "        if(cap.type = 'add-to-cart', 1, 0) as  add_to_cart, \n",
    "        if(cap.type = 'purchase', 1, 0) as purchase\n",
    "      from signals cap \n",
    "      where (cap.type != 'query')\n",
    "    ) raw_signals\n",
    "  group by user, product\n",
    ") as per_type_boosts\n",
    "\"\"\"\n",
    "\n",
    "aggregate_signals(signals_collection, signals_aggregation_collection, mixed_signal_types_aggregation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "606ccba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_prefs_options={\"zkhost\": \"aips-zk\", \"collection\": signals_aggregation_collection}\n",
    "df = spark.read.format(\"solr\").options(**user_prefs_options).load()\n",
    "df.registerTempTable(\"user_product_implicit_preferences\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e289dcf3",
   "metadata": {},
   "source": [
    "# !!!DELETE UNTIL NEXT NOTICE!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6d297d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#product_dataframe=spark.sql(\"select user, product, boost from user_product_implicit_preferences\")\n",
    "#product_dataframe.createOrReplaceTempView(\"raw_user_item_prefs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b36df586",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_prefs = spark.sql(\"\"\"select user, product, rating from user_product_implicit_preferences\n",
    "                          where product in (\n",
    "                              select product from (\n",
    "                                  select product, count(user) user_count from user_product_implicit_preferences\n",
    "                                  group by product\n",
    "                                  limit 10000\n",
    "                              ) top_products\n",
    "                           )   \n",
    "                       order by rating desc\"\"\")\n",
    "#user_prefs.createOrReplaceTempView(\"top_product_user_prefs\")\n",
    "#spark.sql(\"\"\"select count(*) from top_product_user_prefs\"\"\").show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "069af200",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/spark/python/pyspark/sql/pandas/conversion.py:474: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for column, series in pdf.iteritems():\n",
      "/usr/local/spark/python/pyspark/sql/pandas/conversion.py:486: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for column, series in pdf.iteritems():\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame[userIndex: bigint, productIndex: bigint, rating: bigint]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning model training\n",
      "Beginning predictions\n",
      "Beginning evaluation\n",
      "Root-mean-square error = 0.2831574136463788\n"
     ]
    }
   ],
   "source": [
    "#book images example\n",
    "from pyspark.ml.recommendation import ALS\n",
    "import pandas \n",
    "import io\n",
    "from pyspark.sql import Row\n",
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "\n",
    "\n",
    "user_item_prefs = \"\"\"\n",
    "userIndex,productIndex,rating\n",
    "0,0,9\n",
    "0,1,7\n",
    "0,3,1\n",
    "0,4,7\n",
    "1,0,2\n",
    "1,2,9\n",
    "1,3,10\n",
    "1,5,10\n",
    "2,0,9\n",
    "2,1,6\n",
    "2,2,3\n",
    "2,3,4\n",
    "2,4,8\n",
    "2,5,1\n",
    "3,0,10\n",
    "3,1,7\n",
    "3,2,6\n",
    "3,3,8\n",
    "3,4,1\n",
    "3,5,9\n",
    "4,0,1\n",
    "4,1,3\n",
    "4,2,9\n",
    "4,4,1\n",
    "4,5,7\n",
    "\"\"\"\n",
    "\n",
    "data = io.StringIO(user_item_prefs)\n",
    "pd_df = pandas.read_csv(data, sep=\",\")\n",
    "df = spark.createDataFrame(pd_df)\n",
    "display(df)\n",
    "\n",
    "\n",
    "als = ALS(maxIter=10, rank=3, regParam=0.10, implicitPrefs=False, userCol=\"userIndex\", itemCol=\"productIndex\", ratingCol=\"rating\", coldStartStrategy=\"drop\")\n",
    "\n",
    "#prefsRDD = df.rdd.map(lambda p: Row(userIndex=int(p[0]), productIndex=int(p[1]),\n",
    "#                                     boost=float(p[2])))\n",
    "\n",
    "#prefsRDD.take(10)\n",
    "#clean_user_prefs = spark.createDataFrame(prefsRDD)\n",
    "\n",
    "clean_user_prefs = df\n",
    "\n",
    "(training, test) = clean_user_prefs.randomSplit([0.8, 0.2])\n",
    "#training.take(10)\n",
    "#test.take(10)\n",
    "\n",
    "print(\"Beginning model training\")\n",
    "model = als.fit(clean_user_prefs)\n",
    "\n",
    "print(\"Beginning predictions\")\n",
    "predictions = model.transform(clean_user_prefs)\n",
    "\n",
    "print(\"Beginning evaluation\")\n",
    "evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\",\n",
    "                                predictionCol=\"prediction\")\n",
    "rmse = evaluator.evaluate(predictions)\n",
    "print(\"Root-mean-square error = \" + str(rmse))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7b913e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "users: \n",
      "+---+-----------------------------------+\n",
      "|id |features                           |\n",
      "+---+-----------------------------------+\n",
      "|0  |[-0.6238874, -0.9196519, 1.8258095]|\n",
      "|1  |[1.3666669, 1.5634598, 0.6706434]  |\n",
      "|2  |[0.33266556, -1.0720264, 1.8715069]|\n",
      "|3  |[-0.42340577, 1.4799023, 1.9289703]|\n",
      "|4  |[2.0163543, 0.39222753, 0.63329613]|\n",
      "+---+-----------------------------------+\n",
      "\n",
      "None\n",
      "items: \n",
      "+---+------------------------------------+\n",
      "|id |features                            |\n",
      "+---+------------------------------------+\n",
      "|0  |[-0.86946046, 0.043134067, 4.779752]|\n",
      "|1  |[0.18344511, 0.12723537, 3.5788786] |\n",
      "|2  |[3.238656, 2.0716133, 2.226322]     |\n",
      "|3  |[2.7843966, 2.5188313, 2.8806121]   |\n",
      "|4  |[0.14099391, -2.5484672, 2.6138852] |\n",
      "|5  |[2.0812488, 3.5634575, 2.3043165]   |\n",
      "+---+------------------------------------+\n",
      "\n",
      "None\n",
      "predictions: \n",
      "+---------+------------+------+----------+\n",
      "|userIndex|productIndex|rating|prediction|\n",
      "+---------+------------+------+----------+\n",
      "|        0|           0|     9|  9.229693|\n",
      "|        0|           4|     7| 7.0281944|\n",
      "|        0|           1|     7|  6.302889|\n",
      "|        0|           3|     1| 1.2058511|\n",
      "|        1|           5|    10|  9.961071|\n",
      "|        1|           3|    10|  9.675298|\n",
      "|        1|           2|     9|  9.158116|\n",
      "|        1|           0|     2| 2.0846844|\n",
      "|        2|           0|     9|  8.609858|\n",
      "|        2|           4|     8|  7.670832|\n",
      "|        2|           1|     6| 6.6225224|\n",
      "|        2|           3|     4|  3.617105|\n",
      "|        2|           2|     3| 3.0231423|\n",
      "|        2|           5|     1| 1.1847839|\n",
      "|        3|           0|    10|  9.651968|\n",
      "|        3|           5|     9|  8.837315|\n",
      "|        3|           3|     8|   8.10531|\n",
      "|        3|           1|     7|  7.014175|\n",
      "|        3|           2|     6| 5.9890285|\n",
      "|        3|           4|     1| 1.2109272|\n",
      "|        4|           2|     9|  8.752743|\n",
      "|        4|           5|     7|  7.053536|\n",
      "|        4|           1|     3| 2.6862855|\n",
      "|        4|           0|     1| 1.2907764|\n",
      "|        4|           4|     1|  0.940078|\n",
      "+---------+------------+------+----------+\n",
      "\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "#model.userFeatures.collect().foreach{case (productID,latentFactors) => println(\"proID:\"+ productID + \" factors:\"+ latentFactors.mkString(\",\") )}\n",
    "#model.productFeatures.collect().foreach{case (productID,latentFactors) => println(\"proID:\"+ productID + \" factors:\"+ latentFactors.mkString(\",\") )}\n",
    "import numpy\n",
    "import pandas \n",
    "from pyspark.sql.functions import col\n",
    "\n",
    "matrix_u = model.userFactors\n",
    "print(\"users: \")\n",
    "print(matrix_u.show(10,False))\n",
    "matrix_i = model.itemFactors\n",
    "print(\"items: \")\n",
    "print(matrix_i.show(10,False))\n",
    "#am = numpy.dot(matrix_u.rdd, matrix_i.rdd)\n",
    "print(\"predictions: \")\n",
    "print(predictions.orderBy(col(\"userIndex\").asc(),col(\"prediction\").desc()).show(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "29ac6505",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "\n",
    "def matrix_factorization(R, P, Q, K, steps=5000, alpha=0.0002, beta=0.02):\n",
    "    '''\n",
    "    R: rating matrix\n",
    "    P: |U| * K (User features matrix)\n",
    "    Q: |D| * K (Item features matrix)\n",
    "    K: latent features\n",
    "    steps: iterations\n",
    "    alpha: learning rate\n",
    "    beta: regularization parameter'''\n",
    "    Q = Q.T\n",
    "\n",
    "    for step in range(steps):\n",
    "        for i in range(len(R)):\n",
    "            for j in range(len(R[i])):\n",
    "                if R[i][j] > 0:\n",
    "                    # calculate error\n",
    "                    eij = R[i][j] - numpy.dot(P[i,:],Q[:,j])\n",
    "\n",
    "                    for k in range(K):\n",
    "                        # calculate gradient with a and beta parameter\n",
    "                        P[i][k] = P[i][k] + alpha * (2 * eij * Q[k][j] - beta * P[i][k])\n",
    "                        Q[k][j] = Q[k][j] + alpha * (2 * eij * P[i][k] - beta * Q[k][j])\n",
    "\n",
    "        eR = numpy.dot(P,Q)\n",
    "\n",
    "        e = 0\n",
    "\n",
    "        for i in range(len(R)):\n",
    "\n",
    "            for j in range(len(R[i])):\n",
    "\n",
    "                if R[i][j] > 0:\n",
    "\n",
    "                    e = e + pow(R[i][j] - numpy.dot(P[i,:],Q[:,j]), 2)\n",
    "\n",
    "                    for k in range(K):\n",
    "\n",
    "                        e = e + (beta/2) * (pow(P[i][k],2) + pow(Q[k][j],2))\n",
    "        # 0.001: local minimum\n",
    "        if e < 0.001:\n",
    "\n",
    "            break\n",
    "\n",
    "    return P, Q.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0d665ace",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "R = [\n",
    "\n",
    "     [9,7,-1,1,7,-1],\n",
    "\n",
    "     [2,-1,9,10,-1,10],\n",
    "\n",
    "     [9,6,3,4,8,1],\n",
    "\n",
    "     [10,7,6,8,1,9],\n",
    "\n",
    "     [1,3,9,-1,1,7]\n",
    "\n",
    "    ]\n",
    "\n",
    "R = numpy.array(R)\n",
    "# N: num of User\n",
    "N = len(R)\n",
    "# M: num of Movie\n",
    "M = len(R[0])\n",
    "# Num of Features\n",
    "K = 3\n",
    "\n",
    "numpy.random.seed(0) #ONLY for reproducibility purposes for example. Comment out in production code.\n",
    "P = numpy.random.rand(N,K)\n",
    "Q = numpy.random.rand(M,K)\n",
    "\n",
    " \n",
    "\n",
    "nP, nQ = matrix_factorization(R, P, Q, K)\n",
    "\n",
    "nR = numpy.dot(nP, nQ.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5872396a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.6739399  -0.51070211  2.81117838]\n",
      " [ 1.13126403  3.1778382  -0.1336868 ]\n",
      " [ 1.66397212 -0.07945968  2.50026981]\n",
      " [-0.04713731  2.76212533  2.28584453]\n",
      " [ 2.20404797  2.1608718  -0.20048418]]\n",
      "[[ 0.09137152  0.7479391   3.43067962]\n",
      " [ 0.70582232  0.7495681   2.20931952]\n",
      " [ 1.91215473  2.17472719  0.01406682]\n",
      " [ 1.74290933  2.54408653  0.45622567]\n",
      " [ 1.80440229 -1.16920051  1.88968473]\n",
      " [ 0.28111633  3.0233993   0.29501458]]\n",
      "[[ 9.32385721  6.30366709  0.21758395  1.15787754  7.12541272 -0.52526228]\n",
      " [ 2.02155816  2.88512067  9.07220245  9.99539463 -1.92690055  9.88645103]\n",
      " [ 8.67023335  6.63880312  3.04413988  3.83868752  7.82010108  0.96514744]\n",
      " [ 9.90359475  7.0872914   5.94888979  7.98779074  1.00499247  9.01211419]\n",
      " [ 1.12979073  2.7324532   8.91096721  9.24743456  1.07164489  7.0936264 ]]\n"
     ]
    }
   ],
   "source": [
    "print(P)\n",
    "print(Q)\n",
    "print(nR)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab4c175",
   "metadata": {},
   "source": [
    "# !!! Stop Deleting !!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "872a4fa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Time: 2023-09-11 14:50:44\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "print (\"Start Time: \" + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c4d19491",
   "metadata": {},
   "outputs": [],
   "source": [
    "##50K = all products\n",
    "#This will take long time. Recommend setting to 1,000 if trying to just run through code\n",
    "#without considering all products\n",
    "\n",
    "top_product_count_for_recs = 50000 #all products. \n",
    "\n",
    "user_item_ratings = spark.sql(f\"\"\"\n",
    "  select user, product, rating from user_product_implicit_preferences\n",
    "  where product in (\n",
    "    select product from (\n",
    "      select product, count(user) user_count from user_product_implicit_preferences\n",
    "      group by product\n",
    "      limit {top_product_count_for_recs}\n",
    "    ) top_products\n",
    "  )   \n",
    "  order by rating desc\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4920cdad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import StringIndexer\n",
    "from pyspark.ml.feature import IndexToString\n",
    "\n",
    "userIndexer = StringIndexer(inputCol=\"user\", outputCol=\"userIndex\").fit(user_item_ratings)\n",
    "productIndexer = StringIndexer(inputCol=\"product\", outputCol=\"productIndex\").fit(user_item_ratings)\n",
    "\n",
    "def stringsToIndexes(user_item_ratings,user_indexer,product_indexer):\n",
    "    #Fits a model to the input dataset with optional parameters.\n",
    "    return product_indexer.transform(user_indexer.transform(user_item_ratings))\n",
    "    \n",
    "def indexesToStrings(user_item_ratings,user_indexer,product_indexer):\n",
    "    userIndexToString = IndexToString(inputCol=\"userIndex\", outputCol=\"user\",\n",
    "                                    labels=user_indexer.labels)\n",
    "    \n",
    "    productIndexToString = IndexToString(inputCol=\"productIndex\", outputCol=\"product\",\n",
    "                                    labels=product_indexer.labels)\n",
    "\n",
    "    return userIndexToString.transform(productIndexToString.transform(user_item_ratings))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1b02af67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------+------+---------+------------+\n",
      "|   user|     product|rating|userIndex|productIndex|\n",
      "+-------+------------+------+---------+------------+\n",
      "|u159789|024543718710|     1|      0.0|       263.0|\n",
      "|u159789|018713571687|     1|      0.0|     10355.0|\n",
      "|u159789|008888345435|     1|      0.0|      5073.0|\n",
      "|u159789|813985010007|     1|      0.0|      5819.0|\n",
      "|u159789|801213001996|     1|      0.0|     28736.0|\n",
      "|u159789|886541105851|     1|      0.0|      1394.0|\n",
      "|u159789|886112115302|     1|      0.0|       548.0|\n",
      "|u159789|720616236029|     1|      0.0|      2781.0|\n",
      "|u159789|025192979620|     1|      0.0|     12289.0|\n",
      "|u159789|025193102324|     1|      0.0|      9650.0|\n",
      "+-------+------------+------+---------+------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "indexedUserPrefs = stringsToIndexes(user_item_ratings,userIndexer,productIndexer)\n",
    "indexedUserPrefs.orderBy(col(\"userIndex\").asc(),col(\"rating\").desc()).show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b6b6a2fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning model training\n",
      "Beginning predictions\n",
      "Beginning evaluation\n",
      "Root-mean-square error = 0.9597145645338756\n"
     ]
    }
   ],
   "source": [
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "from pyspark.ml.recommendation import ALS\n",
    "from pyspark.sql import Row\n",
    "\n",
    "als = ALS(maxIter=3, rank=10, regParam=0.15, implicitPrefs=True, userCol=\"userIndex\", itemCol=\"productIndex\", ratingCol=\"rating\", coldStartStrategy=\"drop\")\n",
    "\n",
    "(training, test) = indexedUserPrefs.randomSplit([0.8, 0.2])\n",
    "\n",
    "print(\"Beginning model training\")\n",
    "model = als.fit(indexedUserPrefs)\n",
    "\n",
    "print(\"Beginning predictions\")\n",
    "predictions = model.transform(test)\n",
    "\n",
    "print(\"Beginning evaluation\")\n",
    "evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=\"rating\",\n",
    "                                predictionCol=\"prediction\")\n",
    "rmse = evaluator.evaluate(predictions)\n",
    "print(\"Root-mean-square error = \" + str(rmse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "561d70cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+--------------------+\n",
      "|userIndex|     recommendations|\n",
      "+---------+--------------------+\n",
      "|       26|[{3, 0.07131122},...|\n",
      "|       27|[{9, 0.006997348}...|\n",
      "|       28|[{11, 0.10574821}...|\n",
      "|       31|[{7, 0.017309517}...|\n",
      "|       34|[{9, 0.00690294},...|\n",
      "|       44|[{3, 0.03240246},...|\n",
      "|       53|[{13, 0.023354078...|\n",
      "|       65|[{7, 0.035253286}...|\n",
      "|       76|[{1, 0.020188266}...|\n",
      "|       78|[{16, 0.03562626}...|\n",
      "+---------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n",
      "DataFrame[user: string, product: string, rating: bigint, userIndex: double, productIndex: double, prediction: float]\n"
     ]
    }
   ],
   "source": [
    "# Generate top 3 product recommendations for each user\n",
    "indexedUserRecs = model.recommendForAllUsers(10)\n",
    "indexedUserRecs.show(10)\n",
    "\n",
    "\n",
    "print(predictions.orderBy(col(\"userIndex\").asc(),col(\"prediction\").desc()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "67f5db2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import explode, col\n",
    "\n",
    "exploded = (indexedUserRecs.select(\"userIndex\", explode(\"recommendations\").alias(\"productIndex_rating\"))).select(\"userIndex\", col(\"productIndex_rating.*\"))\n",
    "userItemRecs = indexesToStrings(exploded,userIndexer,productIndexer).select(\"user\", \"product\", col(\"prediction\").alias(\"boost\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4f26bb58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Time: 2023-09-10 03:01:24\n",
      "Wiping 'user_item_recommendations' collection\n",
      "[('action', 'CREATE'), ('name', 'user_item_recommendations'), ('numShards', 1), ('replicationFactor', 1)]\n",
      "Creating 'user_item_recommendations' collection\n",
      "Status: Success\n",
      "Writing recommendations to Solr...\n",
      "Recommendations saved to table: user_item_recommendations\n",
      "End Time: 2023-09-10 03:10:53\n"
     ]
    }
   ],
   "source": [
    "print (\"Start Time: \" + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "\n",
    "#Write recommendations to Solr\n",
    "def write_boosts_signals(recommendations_collection, userItemRecs):\n",
    "\n",
    "    create_collection(recommendations_collection)\n",
    "\n",
    "    print(\"Writing recommendations to Solr...\")\n",
    "    signals_opts={\"zkhost\": \"aips-zk\", \"collection\": recommendations_collection}\n",
    "    opts={\"zkhost\": \"aips-zk\", \"collection\": recommendations_collection, \"gen_uniq_key\": \"true\", \"commit_within\": \"5000\"}\n",
    "    userItemRecs.registerTempTable(\"temp_user_item_recs\") #faster to use spark.sql than write df directly due to execution plan\n",
    "    spark.sql(\"select * from temp_user_item_recs\").write.format(\"solr\").options(**opts).mode(\"overwrite\").save()\n",
    "    #userItemRecs.write.format(\"solr\").options(**opts).mode(\"overwrite\").save()\n",
    "    print(\"Recommendations saved to table: \" + recommendations_collection)\n",
    "    \n",
    "write_boosts_signals(\"user_item_recommendations\", userItemRecs)\n",
    "\n",
    "print (\"End Time: \" + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0d5c97e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "End Time: 2023-09-10 03:10:53\n"
     ]
    }
   ],
   "source": [
    "print (\"End Time: \" + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba10513",
   "metadata": {},
   "source": [
    "# Search with Recommendations Boosts\n",
    "Whereas signals boosting boosts the most popular documents for a particular query (ch8), you can also boost the most personalized items for a particular user. In order to serve up the pre-generated collaborative recommendations we just generated, we can just need to run a search and boost the recommended items for each user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "3049fd5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "User: u478462\n",
      "\n",
      "Previous Searches: \n",
      "--apple\n",
      "--macbook\n",
      "\n",
      "Previous Product Interactions: \n",
      "--type: click, name: Apple® - iPad® 2 with Wi-Fi - 16GB - Black\n",
      "--type: add-to-cart, name: Apple® - iPad® 2 with Wi-Fi - 16GB - Black\n",
      "--type: purchase, name: Apple® - iPad® 2 with Wi-Fi - 16GB - Black\n",
      "--type: click, name: Apple® - MacBook® Air - Intel® Core™ i5 Processor - 11.6\" Display - 4GB Memory - 128GB Flash Storage\n"
     ]
    }
   ],
   "source": [
    "def search(collection=\"products\", query=\"*:*\", user=\"\", \n",
    "           filters=[], sort=\"\", fields=[\"*\"], params={}):\n",
    "    return requests.post(\n",
    "             f\"{SOLR_URL}/{collection}/select\", json={\n",
    "               \"query\":query, \"filter\": filters, \"sort\": sort, \n",
    "               \"fields\": fields, \"params\": params}\n",
    "           ).json()[\"response\"]\n",
    "\n",
    "signals_collection = \"signals\"\n",
    "previous_searches = search(collection=signals_collection, \n",
    "                           filters=[\"user:\" + user, \"type:query\"], \n",
    "                           sort=\"signal_time asc\")[\"docs\"]\n",
    "product_interactions = search(\n",
    "                                collection=signals_collection, \n",
    "                                filters=[\"user:\" + user, \"-type:query\"], \n",
    "                                sort=\"signal_time asc\")[\"docs\"]\n",
    "\n",
    "interacted_products = \"\"\n",
    "for interaction in product_interactions:\n",
    "    if len(interacted_products) > 0: interacted_products += \" \"\n",
    "    interacted_products += interaction[\"target\"]\n",
    "\n",
    "product_search = search(\n",
    "                    filters=[\"upc:(\" + interacted_products + \")\"]\n",
    "                 )[\"docs\"]\n",
    "product_info = {}\n",
    "for product in product_search:\n",
    "    product_info[product[\"upc\"]] = product[\"name\"]\\\n",
    "        .replace(\"&#xAE;\",\"®\").replace(\"&#x2122;\", \"™\") #replace encoded version to make more readable\n",
    "\n",
    "#Example User:\n",
    "user = \"u478462\"\n",
    "print(\"User: \" + str(user))\n",
    "    \n",
    "print(\"\\nPrevious Searches: \")\n",
    "for doc in previous_searches:\n",
    "    print(\"--\" + doc[\"target\"])\n",
    "    \n",
    "print(\"\\nPrevious Product Interactions: \")\n",
    "for interaction in product_interactions:\n",
    "    print(\"--type: \" + interaction[\"type\"] + \", name: \" \n",
    "                     + product_info[interaction[\"target\"]]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "8a28fe58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boost Query:\n",
      "\"885909457588\"^74.14599 \"848447000135\"^39.17926 \"848447000081\"^29.756528 \"097361455143\"^26.033008000000002 \"884116069973\"^25.859052 \"848447000005\"^25.679642 \"635753490879\"^24.608636 \"886111778287\"^23.006012000000002 \"097361455044\"^22.929268 \"885909446070\"^21.848762\n",
      "\n",
      "Recommendations: \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div id=\"demo\">\n",
       "        <input style=\"width:50%\" readonly type=\"text\" name=\"q\" value=\"\">\n",
       "        <input readonly type=\"submit\" value=\"Search\">\n",
       "\n",
       "    <div class=\"results\">\n",
       "    \t\n",
       "\n",
       "    </div>\n",
       "</div>\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/885909457588.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Apple&#xAE; - iPad&#xAE; 2 with Wi-Fi - 16GB - Black | <strong>Manufacturer:</strong> Apple&#xAE;</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/848447000135.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Beats By Dr. Dre - Beats Solo HD Over-the-Ear Headphones - Black | <strong>Manufacturer:</strong> Beats By Dr. Dre</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/848447000081.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Beats By Dr. Dre - Beats Tour Earbud Headphones - Black | <strong>Manufacturer:</strong> Beats By Dr. Dre</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/884116069973.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Dell - Inspiron Laptop / AMD Athlon&#x2122; II X2 Processor / 15.6\" Display / 4GB Memory - Obsidian Black | <strong>Manufacturer:</strong> Dell</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/848447000005.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Beats By Dr. Dre - Beats Studio Over-the-Ear Headphones - Black | <strong>Manufacturer:</strong> Beats By Dr. Dre</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "user = \"u478462\"\n",
    "user_item_recs_collection = \"user_item_recommendations\"\n",
    "\n",
    "def get_query_time_boosts(user, boosts_collection):\n",
    "    user_filter = [\"user:\" + user] if user else []\n",
    "\n",
    "    signals_boosts = search(\n",
    "            boosts_collection, filters=user_filter, sort=\"boost desc\", \n",
    "            fields = [\"product\", \"boost\"], params={\"rows\": 10}\n",
    "        )[\"docs\"]\n",
    "\n",
    "    product_boosts = \"\"\n",
    "    for entry in signals_boosts:\n",
    "        if len(product_boosts) > 0:  product_boosts += \" \"\n",
    "        product_boosts += '\"' + str(entry['product']) \\\n",
    "                              + '\"^' + str(float(entry['boost'])*100)\n",
    "\n",
    "    return product_boosts\n",
    "\n",
    "def run_main_query(query, signals_boosts):     \n",
    "    if not signals_boosts: signals_boosts = \"*:*\"\n",
    "    if not query: query=\"*:*\"\n",
    "        \n",
    "    main_query = search(\n",
    "        query=query, fields=[\"upc\", \"name\", \"manufacturer\", \"score\"],\n",
    "        sort=\"score desc, upc asc\", params={\n",
    "          \"qf\": \"name manufacturer long_description\", \n",
    "          \"defType\": \"edismax\", \"rows\": \"5\",\n",
    "          \"boost\": \"sum(1,query({! df=upc v=$signals_boosting}))\",\n",
    "          \"signals_boosting\": signals_boosts\n",
    "        })    \n",
    "    \n",
    "    return main_query\n",
    "\n",
    "recommendations_boosts = get_query_time_boosts(user, \"user_item_recommendations\")\n",
    "print(\"Boost Query:\\n\" + recommendations_boosts)\n",
    "\n",
    "#recommendations\n",
    "search_results = run_main_query(query=None, signals_boosts=recommendations_boosts)[\"docs\"]\n",
    "print(\"\\nRecommendations: \")\n",
    "display(HTML(render_search_results(\"\", search_results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "5a5189c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Non-personalized Query (q=headphones): \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div id=\"demo\">\n",
       "        <input style=\"width:50%\" readonly type=\"text\" name=\"q\" value=\"headphones\">\n",
       "        <input readonly type=\"submit\" value=\"Search\">\n",
       "\n",
       "    <div class=\"results\">\n",
       "    \t\n",
       "\n",
       "    </div>\n",
       "</div>\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/803238004525.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Headphones - CD | <strong>Manufacturer:</strong> Suicide Squeeze</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/885038024644.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> AKG - Earbud Headphones - Black | <strong>Manufacturer:</strong> AKG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/885038024651.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> AKG - Earbud Headphones - Brown | <strong>Manufacturer:</strong> AKG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/885038021209.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> AKG - Stage and Studio Pro Over-the-Ear Headphones | <strong>Manufacturer:</strong> AKG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/27242755871.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Sony - Earbud Headphones | <strong>Manufacturer:</strong> Sony</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#non-personalized query\n",
    "search_results = run_main_query(query=\"headphones\", signals_boosts=None)[\"docs\"]\n",
    "print(f\"Non-personalized Query (q={query}): \")\n",
    "display(HTML(render_search_results(query, search_results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "594db009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Personalized Query (q=headphones, user=u478462): \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div id=\"demo\">\n",
       "        <input style=\"width:50%\" readonly type=\"text\" name=\"q\" value=\"headphones\">\n",
       "        <input readonly type=\"submit\" value=\"Search\">\n",
       "\n",
       "    <div class=\"results\">\n",
       "    \t\n",
       "\n",
       "    </div>\n",
       "</div>\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/848447000135.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Beats By Dr. Dre - Beats Solo HD Over-the-Ear Headphones - Black | <strong>Manufacturer:</strong> Beats By Dr. Dre</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/848447000081.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Beats By Dr. Dre - Beats Tour Earbud Headphones - Black | <strong>Manufacturer:</strong> Beats By Dr. Dre</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/848447000005.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Beats By Dr. Dre - Beats Studio Over-the-Ear Headphones - Black | <strong>Manufacturer:</strong> Beats By Dr. Dre</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/803238004525.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Headphones - CD | <strong>Manufacturer:</strong> Suicide Squeeze</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/885038024644.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> AKG - Earbud Headphones - Black | <strong>Manufacturer:</strong> AKG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#personalized query\n",
    "search_results = run_main_query(query=\"headphones\", signals_boosts=recommendations_boosts)[\"docs\"]\n",
    "print(f\"Personalized Query (q={query}, user={user}): \")\n",
    "display(HTML(render_search_results(query, search_results)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db01e3f2",
   "metadata": {},
   "source": [
    "# Vector-based Personalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f07b2837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-------------------+-----------+--------------------+--------------------------------+\n",
      "|count(upc)|count(DISTINCT upc)|count(name)|count(DISTINCT name)|count(DISTINCT short_description)|\n",
      "+----------+-------------------+-----------+--------------------+--------------------------------+\n",
      "|     48194|              46155|      48194|               46124|                           19024|\n",
      "+----------+-------------------+-----------+--------------------+--------------------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"select count(upc), count(distinct upc),count(name), count(distinct name), count(distinct short_description) from products g\"\"\").show(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "403e0c2b",
   "metadata": {},
   "source": [
    "# Figure 9.10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "629119a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create the product data set for embeddings \n",
    "product_dataframe=spark.sql(\"\"\"\n",
    "  select distinct name, string(upc), short_description, \n",
    "  concat(name,\" \",short_description ) as name_desc from products\"\"\")\n",
    "product_dataframe.createOrReplaceTempView(\"products_samples\")\n",
    "#product_texts=product_dataframe.select(\"name_desc\").rdd.flatMap(lambda x: x).collect()\n",
    "product_names=product_dataframe.select(\"name\").rdd.flatMap(lambda x: x).collect()\n",
    "product_ids=product_dataframe.select(\"upc\").rdd.flatMap(lambda x: x).collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "37e8f289",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48164, 48164)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(product_ids),len(product_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1ad4cafa",
   "metadata": {},
   "outputs": [
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    {
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      ]
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     "metadata": {},
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    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "transformer = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5af831cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "d8d0c622",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Get the embedding \n",
    "import pickle, os\n",
    "from sentence_transformers import SentenceTransformer\n",
    "## picked this model \"all-mpnet-base-v2\" based on benchmark on this link https://medium.com/@nils_reimers/openai-gpt-3-text-embeddings-really-a-new-state-of-the-art-in-dense-text-embeddings-6571fe3ec9d9\n",
    "transformer = SentenceTransformer('all-mpnet-base-v2')\n",
    "embFileName='../data/product_data_embeddings_all-mpnet-base-v2_names_all_embed.pickle'\n",
    "dataFileName= '../data/product_data_embeddings_all-mpnet-base-v2_names_all_names.pickle'\n",
    "iDsFileName= '../data/product_data_embeddings_all-mpnet-base-v2_names_all_ids.pickle'\n",
    "\n",
    "###\n",
    "def getEmbeddings(texts,ids,ignore_cache=False):\n",
    "    load_from_cache=True\n",
    "    \n",
    "    if ignore_cache or not (os.path.isfile(embFileName) and os.path.isfile(dataFileName) and os.path.isfile(iDsFileName)):\n",
    "        load_from_cache=False  \n",
    "        \n",
    "    if not load_from_cache:\n",
    "        embeddings = transformer.encode(texts, convert_to_tensor=False)\n",
    "        with open(embFileName,'wb') as fd:\n",
    "            pickle.dump(embeddings,fd)\n",
    "        with open(dataFileName,'wb') as fd:\n",
    "            pickle.dump(texts,fd)\n",
    "        with open(iDsFileName,'wb') as fd:\n",
    "            pickle.dump(ids,fd)\n",
    "    else:\n",
    "        with open(embFileName,'rb') as fd:\n",
    "            embeddings = pickle.load(fd)\n",
    "        with open(dataFileName,'rb') as fd:\n",
    "            texts = pickle.load(fd)\n",
    "        with open(iDsFileName,'rb') as fd:\n",
    "            ids = pickle.load(fd)\n",
    "    return embeddings,texts,ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "93c2ceb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#product_embeddings=getEmbeddings(p_texts,load_from_cache=True)\n",
    "#product_embeddings=getEmbeddings(product_names,load_from_cache=True)\n",
    "\n",
    "product_embeddings,product_names,product_ids=getEmbeddings(product_names,product_ids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "efb6d456",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48164, 48164, 48164, 46155)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Product Ids-embedding, Product ID-names dictionaries \n",
    "product_ids_emb = dict(zip(product_ids, product_embeddings))\n",
    "product_ids_names= dict(zip(product_ids, product_names))\n",
    "#len(product_texts), len(product_embeddings), len(product_names) ,len(product_ids),len(product_ids_names.values())\n",
    "len(product_embeddings), len(product_names) ,len(product_ids),len(product_ids_names.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "c8393701",
   "metadata": {},
   "outputs": [],
   "source": [
    "######## Cluster the product info\n",
    "from collections import defaultdict\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn.cluster as cluster\n",
    "import time\n",
    "%matplotlib inline\n",
    "sns.set_context('poster')\n",
    "sns.set_color_codes()\n",
    "plot_kwds = {'alpha' : 0.25, 's' : 80, 'linewidths':0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "6d6e9cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Get the clusters \n",
    "def get_clusters(data, algorithm, args, kwds):\n",
    "    start_time = time.time()\n",
    "    algo=algorithm(*args, **kwds).fit(data)\n",
    "    end_time = time.time()\n",
    "    return algo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "866f9b05",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### K-means clustering\n",
    "algo=get_clusters(product_embeddings, cluster.KMeans, (), {'n_clusters':100, 'n_init':10})\n",
    "labels = algo.predict(product_embeddings)\n",
    "centers=algo.cluster_centers_\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cf105975",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "### AffinityPropagation\n",
    "#algo2=get_clusters(product_embeddings, cluster.AffinityPropagation, (), {})\n",
    "#labels2 = algo2.predict(product_embeddings)\n",
    "#centers2=algo2.cluster_centers_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "705cebc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_clusters(labels,product_names):\n",
    "    clusters={}\n",
    "    clusters = defaultdict(lambda:[],clusters)\n",
    "    for l in range(0,len(labels)):\n",
    "        clusters[labels[l]].append(product_names[l])\n",
    "    return clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "19a2c007",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters=assign_clusters(labels,product_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "2c126980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([24, 53, 50, 30, 54, 73, 44, 57, 16, 28, 72, 26, 81, 63, 58, 75, 35, 82, 27, 25, 84, 61, 48, 74, 29, 12, 42, 59, 34, 18, 56, 32, 62, 0, 49, 5, 17, 90, 71, 22, 70, 13, 6, 10, 7, 67, 21, 78, 83, 20, 60, 39, 91, 2, 1, 37, 51, 38, 66, 47, 11, 45, 52, 92, 4, 36, 15, 31, 88, 64, 77, 33, 80, 23, 9, 14, 41, 68, 99, 85, 86, 89, 96, 97, 46, 40, 79, 87, 55, 65, 95, 98, 94, 43, 93, 3, 76, 69, 8, 19])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "5acb59a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mooz-lum - Widescreen AC3 Dolby - DVD',\n",
       " 'Atrevete A Sonar (4 Disc) - Fullscreen Subtitle Dolby - DVD',\n",
       " 'Hush - Widescreen AC3 Dts - Blu-ray Disc',\n",
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       " 'Vidal Sassoon: The Movie - Widescreen AC3 Dolby - DVD',\n",
       " \"Durdy Game - Widescreen Director's Dolby - DVD\",\n",
       " 'The Ten Commandments - Widescreen AC3 Dolby - DVD',\n",
       " 'THX 1138 - (Ws Dub Rmst Spec Sub Ac3 Dol) - DVD',\n",
       " '40 Days and 40 Nights - Widescreen AC3 Dolby - DVD',\n",
       " 'Avatar: The Last Airbender - Book 3: Fire, Vol. 4 - Fullscreen Dolby - DVD',\n",
       " 'Monterey Pop - Fullscreen Dolby - DVD',\n",
       " 'Any Given Sunday - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Go For It! - Widescreen AC3 Dolby - DVD',\n",
       " 'Damnation Alley - Widescreen Dolby - DVD',\n",
       " 'Almost Famous, Vol. 2 - Fullscreen Dolby - DVD',\n",
       " 'Without a Paddle - Widescreen Subtitle AC3 Dolby - Blu-ray Disc',\n",
       " 'Voltron: The Legend Begins - Fullscreen Dolby - DVD',\n",
       " 'Drumline - Widescreen Dubbed AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Eat Pray Love - Widescreen AC3 Dolby - DVD',\n",
       " 'Clash of the Titans - Widescreen AC3 Dolby - DVD',\n",
       " 'Land Before Time: Amazing Adventures - Fullscreen Dolby - DVD',\n",
       " 'Daniel Tosh: Happy Thoughts - Widescreen AC3 Dolby - DVD',\n",
       " 'Passion Play - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Dirty Love - Subtitle Dolby - DVD',\n",
       " 'Office: The Complete Second Series [WS] - Widescreen Dolby - DVD',\n",
       " \"The Notebook - Widescreen Collector's AC3 Dolby - DVD\",\n",
       " 'The Hip Hop Project - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'WALL-E - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Twelfth Night - Widescreen Dolby - DVD',\n",
       " 'Friday After Next - Widescreen Dolby - DVD',\n",
       " 'Whatever Works - Widescreen AC3 Dolby - DVD',\n",
       " 'Hard Candy - Widescreen Dolby - DVD',\n",
       " 'Christmas Carol: The Musical - Dolby - DVD',\n",
       " \"Set It Off - Widescreen AC3 Director's Dolby - Blu-ray Disc\",\n",
       " 'Get Thrashed - Fullscreen Dolby Special - DVD',\n",
       " 'Life Is Beautiful - Widescreen AC3 Dolby - DVD',\n",
       " 'First Blood - Widescreen Dolby - DVD',\n",
       " 'ABBA: In Concert - AC3 Dolby Dts - DVD',\n",
       " 'Last Days - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Nine Inch Nails: Beside You in Time - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Combat! The Best of New Replacements - Fullscreen Dolby - DVD',\n",
       " 'Zodiac (2005) - Fullscreen AC3 Dolby - DVD',\n",
       " 'Hesher - AC3 Dolby - DVD',\n",
       " 'Scary Movie 3 - Widescreen AC3 Dolby - DVD',\n",
       " 'Teen Wolf/Teen Wolf Too - Widescreen Dolby - DVD',\n",
       " 'Handy Manny: Fixing it Right - Fullscreen Dubbed AC3 Dolby - DVD',\n",
       " 'The Ward - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Hoover Street Revival - Widescreen Subtitle AC3 Dolby Dts - DVD',\n",
       " 'Chaos - Widescreen Subtitle AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'The Beastmaster - Widescreen Dolby Dts Special - DVD',\n",
       " \"Top Gun - Widescreen Collector's Dolby Dts - DVD\",\n",
       " 'Mad Monster Party - Fullscreen Dolby Special - DVD',\n",
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       " 'Steve Byrne: The Byrne Identity - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Fourplay: Live in Cape Town - Fullscreen Dolby - DVD',\n",
       " \"The Notebook - Widescreen Collector's AC3 Dolby - Blu-ray Disc\",\n",
       " 'Stargate Atlantis: The Rising - Widescreen AC3 Dolby - DVD',\n",
       " 'Lost Boys: The Thirst - Widescreen Dolby - DVD',\n",
       " 'Saw II - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Santa Claus Is Coming to Town - Fullscreen AC3 Dolby - DVD',\n",
       " 'Hunger - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Paranormal Entity - Widescreen AC3 Dolby - DVD',\n",
       " 'Bill Burr: Why Do I Do This? - Widescreen AC3 Dolby - DVD',\n",
       " 'The Transporter - Widescreen Subtitle Dolby Dts - Blu-ray Disc',\n",
       " 'Sasquatch Mountain - Dolby - DVD',\n",
       " 'Cirque du Soleil: Alegria - An Enchanting Fable - Widescreen Subtitle Dolby - DVD',\n",
       " 'Jimi Hendrix - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Ladies & Gentlemen - Dolby Dts - Blu-ray Disc',\n",
       " 'WWE: Summerslam 2011 - Fullscreen AC3 Dolby - DVD',\n",
       " 'Dead Can Dance: Toward the Within - Fullscreen Dolby - DVD',\n",
       " 'Koyaanisqatsi - Dolby Dts - DVD',\n",
       " 'Taking Chance - Subtitle AC3 Dolby - DVD',\n",
       " 'Grey Gardens - Widescreen Dolby - DVD',\n",
       " 'Robot Chicken: Star Wars - Episode II - Fullscreen Dolby - DVD',\n",
       " 'Avatar - The Last Airbender: Book 2 - Earth, Vol. 2 - Fullscreen Dubbed Dolby - DVD',\n",
       " 'Yanni: Live - The Concert Event - Widescreen Dolby Dts - DVD',\n",
       " 'The Tempest - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'For Colored Girls - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " \"Set It Off - Widescreen AC3 Director's Dolby - DVD\",\n",
       " 'Very Special Christmas Special - Widescreen Dolby - DVD',\n",
       " 'National Geographic: Collapse - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Ninja Assassin - Widescreen AC3 Dolby - DVD',\n",
       " 'Swimming With Sharks - Widescreen Dolby Special - DVD',\n",
       " 'On Golden Pond - Widescreen Dolby Dts Special - DVD',\n",
       " 'Loving Annabelle - Widescreen Dolby - DVD',\n",
       " 'Skins 1 (3 Disc) - Widescreen Dolby - DVD',\n",
       " 'Incubus: Look Alive - Dolby - DVD',\n",
       " 'Pure 80s Rock - Dolby - DVD',\n",
       " 'Fletch - Widescreen Subtitle AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Plug Me In (2 Disc) (W/Book) - Widescreen Dolby - DVD',\n",
       " 'Attack the Block - Widescreen AC3 Dolby - DVD',\n",
       " 'Hereafter - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'The Collector - Widescreen AC3 Dolby - DVD',\n",
       " 'Roary The Racing Car - Fullscreen Dolby - DVD',\n",
       " 'Fireplace: Visions of Tranquility - Dolby - DVD',\n",
       " 'Tinker Bell - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'The Phantom - Widescreen Subtitle AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'BET Hip Hop Awards 2007 - Fullscreen Dolby - DVD',\n",
       " 'Heaven Is a Playground - Fullscreen Dolby - DVD',\n",
       " 'Legend (Amaray Case) / (Dol) - Dolby - DVD',\n",
       " 'Ironclad - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Three Kings - Widescreen Subtitle Dolby Dts - Blu-ray Disc',\n",
       " 'Creepshow 2 - Widescreen Dolby - DVD',\n",
       " 'No Country for Old Men - Widescreen AC3 Dolby - DVD',\n",
       " 'Seven - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Smiley Face - Dolby - DVD',\n",
       " 'Voltron: The Final Battle - Fullscreen Dolby - DVD',\n",
       " 'Freedom (4 Disc) - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Live At Reading - AC3 Dolby Dts - DVD',\n",
       " 'Ed Wood - Widescreen B&W Subtitle AC3 Dolby - DVD',\n",
       " 'Torchwood: Children of Earth - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'The Heart Specialist - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'The Wedding Singer - Widescreen Subtitle AC3 Dolby Dts - DVD',\n",
       " 'Rush Hour 2 - Widescreen Dolby Dts - DVD',\n",
       " \"Notebook (W/Book) - Widescreen Collector's AC3 Dolby - Blu-ray Disc\",\n",
       " 'Brotherly Love - Subtitle Dolby - DVD',\n",
       " 'Miracle at St. Anna - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Video Games Live: Level 2 - Widescreen AC3 Dolby - DVD',\n",
       " 'Take Me Home Tonight - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Dark House - Widescreen AC3 Dolby - DVD',\n",
       " 'Danny Bhoy: Subject to Change - Widescreen Dolby - DVD',\n",
       " 'Scream - Widescreen AC3 Dolby - DVD',\n",
       " 'The Reagans - Widescreen Dolby - DVD',\n",
       " 'The Ultimate Wave: Tahiti 3D - Widescreen Dubbed AC3 Dolby - Blu-ray Disc',\n",
       " 'Anguish - Widescreen Dolby - DVD',\n",
       " 'Malevolence - Widescreen Dolby - DVD',\n",
       " 'The Lord of the Rings - Fullscreen AC3 Dolby - DVD',\n",
       " 'The Lord of the Rings - Fullscreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Drop Dead Gorgeous - Fullscreen AC3 Dolby - DVD',\n",
       " 'Scenes From The Big Chair - AC3 Dolby - DVD',\n",
       " 'Greek: Chapter Three [3 Discs] - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Seven - AC3 Dolby - Blu-ray Disc',\n",
       " 'More Than a Game - Widescreen AC3 Dolby - DVD',\n",
       " 'City of God - Widescreen AC3 Dolby - DVD',\n",
       " 'The Wicker Man - Widescreen AC3 Dolby - DVD',\n",
       " 'WALL-E - Widescreen AC3 Dolby - DVD',\n",
       " 'Cowboy Bebop: The Movie - Widescreen Subtitle Dolby Dts - Blu-ray Disc',\n",
       " 'True Blood - Dolby - DVD',\n",
       " 'The Gods Must Be Crazy 2 - Widescreen Subtitle Dolby - DVD',\n",
       " 'Yo Gabba Gabba!: Halloween! - Fullscreen Dolby - DVD',\n",
       " 'The Invention of Lying - Widescreen Dolby - DVD',\n",
       " 'The Losers - Widescreen Dolby - DVD',\n",
       " 'WWE: Elimination Chamber 2011 - Fullscreen AC3 Dolby - DVD',\n",
       " 'Arthur - Widescreen AC3 Dolby - DVD',\n",
       " 'The Little Drummer Boy - Fullscreen AC3 Dolby - DVD',\n",
       " 'My Neighbor Totoro - Widescreen Dubbed Dolby Thx - DVD',\n",
       " 'Side Out - Widescreen Dolby - DVD',\n",
       " 'Good Will Hunting - Widescreen AC3 Dolby - DVD',\n",
       " 'ERGO PROXY 1: AWAKENING / (WS SPEC SUB DOL BOX) - Widescreen Box Dolby Special - DVD',\n",
       " 'Harry Brown - Widescreen AC3 Dolby - DVD',\n",
       " 'New York, I Love You - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Under the Sea 3D - Widescreen Fullscreen AC3 Dolby - DVD',\n",
       " 'Ralphie May: Austin-tatious - Widescreen Dolby - DVD',\n",
       " 'I Will Follow - Widescreen Dolby - DVD',\n",
       " 'Mitch Fatel: Mitch Fatel Is Magical - Widescreen Dolby - DVD',\n",
       " 'House 2: The Second Story - Widescreen Dolby - DVD',\n",
       " 'Arthur - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Big Money Rustlas - Widescreen AC3 Dolby - DVD',\n",
       " 'Camp Rock - Fullscreen AC3 Dolby - DVD',\n",
       " 'Kevin Smith: Smodimations Season 1 - Widescreen Dolby - DVD',\n",
       " 'Doubt - Widescreen AC3 Dolby - DVD',\n",
       " 'Poseidon - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Martial Arts of Shaolin - Widescreen Dubbed Dolby - DVD',\n",
       " 'Dolby - DVD',\n",
       " 'Deadland - AC3 Dolby - DVD',\n",
       " 'Elvira, Mistress of the Dark - Widescreen Dolby - DVD',\n",
       " '100 Days - Fullscreen Dolby - DVD',\n",
       " 'Reina Del Sur 1 (6pc) - Widescreen Dolby - DVD',\n",
       " 'With Honors - Fullscreen Dubbed Dolby - DVD',\n",
       " \"CAILLOU'S FAMILY FAVORITES / (FULL DUB DOL SEN) - Fullscreen Dubbed Dolby - DVD\",\n",
       " 'Glee Live! In Concert! - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'The Temptations - Fullscreen Dolby - DVD',\n",
       " 'Pulp Fiction - Widescreen AC3 Dolby - DVD',\n",
       " 'The Harder They Come - Dolby - DVD',\n",
       " 'Life as We Know It - Widescreen AC3 Dolby - DVD',\n",
       " 'The Ward - Widescreen Dolby - DVD',\n",
       " 'Shrek - Fullscreen AC3 Dolby - DVD',\n",
       " 'Sword of the Stranger - AC3 Dolby - DVD',\n",
       " 'Humanoids from the Deep - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Constantine - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Direct Your Own Damn Movie (4 Disc) - Box Dolby - DVD',\n",
       " 'Complete Book 1 Collection (Box Set) (6pc) - Dolby - DVD',\n",
       " 'Animusic - Dolby Special - DVD',\n",
       " 'Mr. Brooks - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Friday the 13th - Widescreen Dolby - DVD',\n",
       " 'Normal - Widescreen Dolby - DVD',\n",
       " 'Ben-Hur - Anniversary Dolby - DVD',\n",
       " \"Top Gun - Fullscreen Collector's Dolby Dts - DVD\",\n",
       " \"Steve Byrne's Happy Hour - Widescreen Dolby - DVD\",\n",
       " 'La Forza del Destino (Teatro alla Scala) - Subtitle Dolby Dts - DVD',\n",
       " 'Knockout - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'North by Northwest - Widescreen AC3 Dolby Special - DVD',\n",
       " 'The Final Countdown - Widescreen Dolby Dts Thx - DVD',\n",
       " 'ECW: Extreme Rules - Fullscreen Dolby - DVD',\n",
       " 'All-Star Superman - Widescreen Dolby - DVD',\n",
       " 'Star Wars Prequel Trilogy [6 Discs] - Widescreen AC3 Dolby Thx - DVD',\n",
       " 'Hell of the Living Dead - Widescreen Dolby - DVD',\n",
       " 'Ready Set Go - Fullscreen Dubbed Dolby - DVD',\n",
       " 'Baa Baa Black Sheep: Volume 1 [2 Pack / Full] - Fullscreen Dolby - DVD',\n",
       " 'Stir of Echoes - Widescreen Subtitle Dolby Dts - Blu-ray Disc',\n",
       " 'The Original Three Tenors Concert - Subtitle AC3 Dolby Dts - DVD',\n",
       " 'Truth Be Told - Widescreen Dolby - DVD',\n",
       " 'In Hell - Dolby - DVD',\n",
       " 'Sid & Nancy - Widescreen AC3 Anniversary Dolby - DVD',\n",
       " 'Spartacus (Moscow Classical  Ballet) - Dolby - DVD',\n",
       " 'Final Destination 3 - Widescreen Dolby Dts Special - DVD',\n",
       " 'Wristcutters: A Love Story - Widescreen AC3 Dolby - DVD',\n",
       " 'The English Patient - Widescreen AC3 Dolby - DVD',\n",
       " 'The Wraith - Dolby - DVD',\n",
       " 'Fracture - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'The Curiosity of Chance - Widescreen AC3 Dolby - DVD',\n",
       " 'Ceremony - Widescreen Subtitle Dolby - Blu-ray Disc',\n",
       " 'The New World - Widescreen AC3 Dolby - DVD',\n",
       " 'Adventureland - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'BIG CATS COLLECTION (2PC) / (WS AC3 DOL) - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'The Thrill of It All! - Widescreen Subtitle Dolby - DVD',\n",
       " 'Chicago - Widescreen AC3 Dolby - DVD',\n",
       " 'Youth in Revolt - Widescreen AC3 Dolby - DVD',\n",
       " 'Jack and the Beanstalk - Dolby - DVD',\n",
       " 'Glory - Widescreen Fullscreen AC3 Dolby - DVD',\n",
       " 'LeapFrog: Numbers Ahoy - Fullscreen Dolby - DVD',\n",
       " 'Jailhouse Rock - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Hellbound: Hellraiser II - Widescreen Dolby - DVD',\n",
       " \"Preacher's Kid - Widescreen Dolby - DVD\",\n",
       " 'Inception - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Game: The Second Sesaon [3 Discs] - Widescreen AC3 Dolby - DVD',\n",
       " 'Airwolf - Fullscreen - DVD',\n",
       " 'The Wereth Eleven - Dolby - DVD',\n",
       " 'The Man Who Cried - Widescreen Dubbed Dolby Dts - DVD',\n",
       " 'Jeff Dunham Show - Widescreen Dolby - DVD',\n",
       " 'Hoodwinked - Fullscreen Dolby - DVD',\n",
       " 'Ja Rule: 2005 - AC3 Dolby - DVD',\n",
       " 'The Matrix - Widescreen AC3 Dolby Special - DVD',\n",
       " 'Yo Gabba Gabba!: Birthday Boogie - Fullscreen Dolby - DVD',\n",
       " \"Mr. Magoo's Christmas Carol - Fullscreen AC3 Dolby - DVD\",\n",
       " \"CAILLOU: CAILLOU'S FUN OUTSIDE / (FULL DOL) - Fullscreen Dolby - DVD\",\n",
       " '35 & Ticking - Widescreen AC3 Dts - Blu-ray Disc',\n",
       " 'Eraserhead - Widescreen B&W Dolby - DVD',\n",
       " 'Seven Days in Utopia - Fullscreen Dolby - DVD',\n",
       " 'Avatar: The Last Airbender - Book 3: Fire, Vol. 3 - Fullscreen Dolby - DVD',\n",
       " 'Dirty Love - Subtitle Dolby - DVD',\n",
       " 'Cinderella Story: Once Upon a Song - Widescreen Dolby - DVD',\n",
       " \"Stephen King's Desperation - Widescreen Dolby - DVD\",\n",
       " 'Scream - Widescreen Subtitle AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'High School Musical 3: Senior Year - Widescreen AC3 Dolby - DVD',\n",
       " 'NASCAR 3D: The IMAX Experience - Subtitle AC3 Dolby - DVD',\n",
       " 'Another Gay Movie - Widescreen Dolby - DVD',\n",
       " 'Scream 2 - Widescreen AC3 Dolby - DVD',\n",
       " 'Peter Pan - Fullscreen Dolby - DVD',\n",
       " \"Preacher's Kid - Widescreen Dolby - Blu-ray Disc\",\n",
       " 'Unit: The Complete Series [19 Discs] - Widescreen Fullscreen AC3 Dolby - DVD',\n",
       " 'LeapFrog: Talking Words Factory - Fullscreen Dolby - DVD',\n",
       " 'Lucky Number Slevin - Fullscreen Dolby Dts - DVD',\n",
       " \"Michael Loftus: You've Changed - Widescreen AC3 Dolby - DVD\",\n",
       " 'Glastonbury - Widescreen Dolby - DVD',\n",
       " 'Farscape: Complete Series (20pc) - Widescreen Box AC3 - Blu-ray Disc',\n",
       " 'Tank - Fullscreen Dolby - DVD',\n",
       " 'Highlander II: The Quickening - Dolby Dts Special - DVD',\n",
       " 'Exotic Saltwater Aquarium Sd - Dolby - DVD',\n",
       " '16 Wishes - Widescreen AC3 Dolby - DVD',\n",
       " 'The Cake Eaters - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'WALL-E - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Hesher - AC3 Dolby - Blu-ray Disc',\n",
       " 'Kate and Leopold - Widescreen AC3 Dolby - DVD',\n",
       " 'TekWar [3 Discs] - Dolby - DVD',\n",
       " 'Living Will - Widescreen AC3 Dolby - DVD',\n",
       " 'Gia - Subtitle Dolby - Blu-ray Disc',\n",
       " 'Inside Out - Widescreen Dolby - DVD',\n",
       " 'Disco Worms - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Rush: Rush in Rio - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'The Blind Side - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Belly - Widescreen Dolby Special - DVD',\n",
       " 'Video Games Live: Level 2 - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Christopher Titus: Love Is Evol - Widescreen AC3 Dolby - DVD',\n",
       " \"Scooby-Doo and the Witch's Ghost - Dubbed AC3 Dolby - DVD\",\n",
       " 'Island of Lost Souls - Widescreen Dolby - DVD',\n",
       " 'The Right Stuff - Widescreen Subtitle Dolby Special - DVD',\n",
       " 'Good Night, and Good Luck. - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Mad Monkey Kung Fu - Widescreen Dubbed Dolby - DVD',\n",
       " 'Inkheart - Widescreen Fullscreen Dolby - DVD',\n",
       " 'The Night Before - Fullscreen Dolby - DVD',\n",
       " 'Tru Calling: The Complete Series [8 Discs] - Widescreen Subtitle Dolby - DVD',\n",
       " 'Yo Gabba Gabba!: Circus - Fullscreen Dolby - DVD',\n",
       " 'Dinosaur - Widescreen Dubbed AC3 Dolby - Blu-ray Disc',\n",
       " 'The Color Purple - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Storm Chasers: Perfect Disaster - Widescreen Dolby - DVD',\n",
       " 'Inception - Widescreen AC3 Dolby - DVD',\n",
       " 'Cher: The Farewell Tour - Widescreen Dolby Dts - DVD',\n",
       " 'Spy Kids - Widescreen AC3 Dolby - DVD',\n",
       " 'Sleepers - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Digital Video Essentials: HD Basics - Dolby - Blu-ray Disc',\n",
       " 'Ben 10: Alien Swarm - Fullscreen AC3 Dolby - DVD',\n",
       " 'The Golden Swallow - Widescreen Dubbed Dolby - DVD',\n",
       " 'Barbie Mermaidia - Widescreen Dolby - DVD',\n",
       " 'Trainspotting - Widescreen Dubbed AC3 Dolby Dts - DVD',\n",
       " 'Ugly Americans 1 - Widescreen AC3 Dolby - DVD',\n",
       " 'Care Bears Classic Quad (2 Disc) - Fullscreen Dolby - DVD',\n",
       " 'Billy Jack - Widescreen Dolby - DVD',\n",
       " 'Friday the 13th - Widescreen Dolby - DVD',\n",
       " 'Earthsea - Fullscreen Dolby - DVD',\n",
       " 'Lucky Number Slevin - Dolby Dts - DVD',\n",
       " 'ABBA: The Movie - AC3 Dolby Dts - DVD',\n",
       " 'Dead Like Me: Life After Death - Widescreen AC3 Dolby - DVD',\n",
       " 'House - Widescreen Dolby - DVD',\n",
       " 'Hereafter - Widescreen AC3 Dolby - DVD',\n",
       " 'The Cell 2 - Widescreen Fullscreen Dolby - DVD',\n",
       " 'Russell Brand in New York City - Widescreen Dolby - DVD',\n",
       " 'Send Me No Flowers - Widescreen Subtitle Dolby - DVD',\n",
       " 'iCarly: iSaved Your Life - Fullscreen Dolby - DVD',\n",
       " 'Attack the Block - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'The Magic Flute (Opera Australia) - Dolby - DVD',\n",
       " 'It Might Get Loud - Widescreen AC3 Dolby - DVD',\n",
       " 'The Proposal - Widescreen AC3 Dolby - DVD',\n",
       " 'Bubba Ho-Tep - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'The Eclipse - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Y Tu Mam&#xE1; Tambi&#xE9;n - Widescreen Subtitle Dolby Special - DVD',\n",
       " \"Dragon Drive, Vol. 8: Ri-On's Plot - Subtitle Dolby - DVD\",\n",
       " 'Monterey Pop - Fullscreen Dolby Dts - DVD',\n",
       " 'Goal! - Widescreen Dubbed AC3 Dolby - Blu-ray Disc',\n",
       " 'Hansel und Gretel (Semperoper Dresden) - Subtitle AC3 Dolby Dts - DVD',\n",
       " 'The Clinic - Widescreen AC3 Dolby - DVD',\n",
       " 'Dirty Dancing: Havana Nights - Widescreen Dolby - DVD',\n",
       " \"Pete's Dragon - Widescreen AC3 Dolby Special - DVD\",\n",
       " 'Scary Movie - Widescreen AC3 Dolby - DVD',\n",
       " 'The Bachelor Party - Widescreen Dolby - DVD',\n",
       " 'The Dark Knight - Fullscreen AC3 Dolby - DVD',\n",
       " 'Dog Day - Dolby - DVD',\n",
       " 'U2: Rattle and Hum - Widescreen Subtitle Dolby Dts - Blu-ray Disc',\n",
       " 'Murda Muzik - Dolby - DVD',\n",
       " 'Journey to Fearless: The... [Blu-ray Disc] - Widescreen Dolby Dts - Blu-ray Disc',\n",
       " 'Cinema Paradiso - Widescreen AC3 Dolby - DVD',\n",
       " '54 - Widescreen AC3 Dolby - DVD',\n",
       " 'Legends of Flight - Widescreen AC3 Dolby - DVD',\n",
       " 'Shakespeare in Love - Widescreen AC3 Dolby - DVD',\n",
       " 'Civilization Of Maxwell Bright - Widescreen Dolby - DVD',\n",
       " 'Yo Gabba Gabba!: Music Makes Me Move! - Fullscreen Dolby - DVD',\n",
       " 'Pablo Francisco: They Put it Out There - Dolby - DVD',\n",
       " 'Super Why: Attack Of The Eraser - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Taylor Swift: Journey to Fearless - Widescreen Dolby Dts - DVD',\n",
       " 'Another Gay Movie - Widescreen Uncut Dolby - DVD',\n",
       " 'Paid in Full - Widescreen AC3 Dolby - DVD',\n",
       " 'Fade to Black - Widescreen Dolby - DVD',\n",
       " 'Spartacus (Australian Ballet) - Dolby - DVD',\n",
       " 'Jeff Dunham: Arguing with Myself - Widescreen Dolby - DVD',\n",
       " 'Infernal Affairs - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Invader Zim: Operation Doom - Fullscreen Dolby - DVD',\n",
       " 'Wooly Boys - Widescreen Subtitle Dolby - DVD',\n",
       " 'Dead and Gone - Widescreen AC3 Dolby - DVD',\n",
       " 'Young Caruso - Fullscreen Dolby - DVD',\n",
       " 'Westinghouse - Widescreen Dolby - DVD',\n",
       " 'The Haunting - Widescreen AC3 Dolby - DVD',\n",
       " 'Jim Jefferies: I Swear to God - Widescreen Dolby - DVD',\n",
       " 'Halo Legends - Fullscreen Dubbed AC3 Dolby Special - DVD',\n",
       " 'Thomas & Friends: Pop Goes Thomas - Fullscreen Dubbed Dolby - DVD',\n",
       " 'Scream Triple Feature [3 Discs] - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Critic: The Entire Series [3 Pack] - Fullscreen Dolby - DVD',\n",
       " 'Louis C.K.: Hilarious - Widescreen AC3 Dolby - DVD',\n",
       " 'Elf - (Full Ws Dub Ac3 Dol) - DVD',\n",
       " 'Tin Man - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Scary Movie 2 - Widescreen AC3 Dolby - DVD',\n",
       " 'Spark Of Insanity - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Brother Bear - Widescreen Dolby Dts Special - DVD',\n",
       " 'Cold Mountain - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Rise of the Gargoyles - Widescreen AC3 Dolby - DVD',\n",
       " 'The Dark Knight - Widescreen AC3 Dolby - DVD',\n",
       " 'Tobor The Great - Fullscreen B&W Dolby - DVD',\n",
       " 'Spin City, Vol. 1 - Fullscreen Dolby - DVD',\n",
       " 'The Girl Who Leapt Through Time - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Scream 3 - Widescreen AC3 Dolby - DVD',\n",
       " 'WWE: Extreme Rules 2011 - Fullscreen AC3 Dolby - DVD',\n",
       " 'Final Destination 3 - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'WALL-E - Widescreen AC3 Dolby - DVD',\n",
       " 'Collective Soul: Home - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Puff the Magic Dragon - Fullscreen Dolby - DVD',\n",
       " 'Reindeer Games - Widescreen AC3 Dolby - DVD',\n",
       " 'Take Me Home Tonight - Widescreen Subtitle AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Sling Blade - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Star Trek: Fan Collectives - Fullscreen AC3 Dolby - DVD',\n",
       " 'Gun - Widescreen AC3 Dolby - DVD',\n",
       " 'Home - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Blast from the Past - Fullscreen Dolby - DVD',\n",
       " 'The Dark Knight - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Toto: Falling in Between Live - Dolby Dts - Blu-ray Disc',\n",
       " \"Initial D (2 Disc) - Widescreen Collector's Dolby Dts - DVD\",\n",
       " 'Cronos - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Denise Austin: Sculpt & Burn Body Blitz - Widescreen Dolby - DVD',\n",
       " 'Ozzfest: 10th Anniversary - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Four Lions - Widescreen Dolby - DVD',\n",
       " 'Contact - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Kill Bill Vol. 1 - Dolby Dts - DVD',\n",
       " 'MI-5, Vol. 9 [3 Discs] - Fullscreen AC3 Dolby - DVD',\n",
       " 'Maniac Cop - Widescreen Dolby Dts Special - DVD',\n",
       " 'The Piano Tuner of Earthquakes - Subtitle Dolby - DVD',\n",
       " 'Hellraiser: Revelations - Dolby - DVD',\n",
       " 'Night of the Living Dead - Dolby - DVD',\n",
       " 'Desk Set - (Ws Dub Sub Dol) - DVD',\n",
       " 'Back to You and Me - Widescreen Dolby - DVD',\n",
       " 'The Jungle Book - Dolby - DVD',\n",
       " 'The Undefeated - Widescreen Dolby - DVD',\n",
       " 'Save the Last Dance 2: Stepping Up - Widescreen Dolby - DVD',\n",
       " 'Dr. T & The Women - Widescreen AC3 Dolby Special - DVD',\n",
       " 'John Witherspoon: You Got to Coordinate - Widescreen Dolby - DVD',\n",
       " 'H.R. Pufnstuf: The Complete Series [3 Discs] - Fullscreen Dolby - DVD',\n",
       " 'Ellen DeGeneres: Here and Now - AC3 Dolby - DVD',\n",
       " 'Little Shop of Horrors - Fullscreen Dolby - DVD',\n",
       " 'Kool and the Gang: Videology - Dolby - DVD',\n",
       " 'Brazil - Widescreen Dolby - DVD',\n",
       " 'Star Knight - Fullscreen Dolby - DVD',\n",
       " \"Michael Jackson'S This Is It - Widescreen Subtitle AC3 Dolby - Blu-ray Disc\",\n",
       " \"Hachi: A Dog's Tale - Widescreen AC3 Dolby - DVD\",\n",
       " 'Red Heat - Widescreen Subtitle Dolby Special - DVD',\n",
       " 'Endure - Widescreen AC3 Dolby - DVD',\n",
       " 'Space Buddies - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Torchwood: Children of Earth - Widescreen AC3 Dolby - DVD',\n",
       " '2012 - Widescreen AC3 Dolby - DVD',\n",
       " 'Broken Lizard: Stands Up - Widescreen Dolby - DVD',\n",
       " 'Saving Private Ryan - Widescreen Dolby Limited Special - DVD',\n",
       " 'Titanic II - Widescreen AC3 Dolby - DVD',\n",
       " 'Little Einsteins: Our Huge Adventure - AC3 Dolby - DVD',\n",
       " 'Roseanne: Halloween Edition - Dolby - DVD',\n",
       " 'WWE: No Way Out 2007 - Fullscreen Dolby - DVD',\n",
       " 'Ultimate Avengers: The Movie - Widescreen Dolby - DVD',\n",
       " 'Repo! The Genetic Opera - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Rosencrantz and Guildenstern Are Dead - Widescreen Dolby - DVD',\n",
       " 'Crow - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Dark Side Of The Moon - Dolby - DVD',\n",
       " 'Until the Light Takes Us - Widescreen Dolby - DVD',\n",
       " \"When You'Re Strange - Dolby Dts - DVD\",\n",
       " 'Unconditional Love - Widescreen Dolby - DVD',\n",
       " 'Dead Heat - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Chicken Little - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'The Blind Side - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'The Other End of the Line - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " \"George Lopez: America's Mexican - Widescreen AC3 Dolby - DVD\",\n",
       " \"TRITON'S REVENGE / (FULL DOL) - Fullscreen Dolby - DVD\",\n",
       " 'Vamp - Widescreen Dolby - DVD',\n",
       " 'Sorority Wars - Widescreen AC3 Dolby - DVD',\n",
       " 'The Others - Widescreen AC3 Dolby - DVD',\n",
       " 'Children of the Corn - Widescreen Dolby - DVD',\n",
       " 'Dance & Hop - Fullscreen Dubbed Dolby - DVD',\n",
       " 'Garfield Show: Spooky Tails - Fullscreen AC3 Dolby - DVD',\n",
       " 'Lucky 7 - (Ws Sub Ac3 Dol) - DVD',\n",
       " 'The Blind Side - Widescreen AC3 Dolby - DVD',\n",
       " \"The Who: Who's Better, Who's Best - AC3 Dolby Dts - DVD\",\n",
       " \"LeapFrog: Let's Go to School - Fullscreen Dolby - DVD\",\n",
       " 'Taxi to the Dark Side - Widescreen AC3 Dolby - DVD',\n",
       " 'The Terminal - (Ws Dub Sub Ac3 Dol Dts) - DVD',\n",
       " 'Metropolis - AC3 Dolby Limited - Blu-ray Disc',\n",
       " \"Sundays at Tiffany's - Widescreen Subtitle AC3 Dolby - DVD\",\n",
       " 'Elephant White - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'OT3P: Live_Confrontation - Dolby - DVD',\n",
       " 'Flowers in the Attic - Widescreen Dolby - DVD',\n",
       " 'Kart Racer - Widescreen AC3 Dolby - DVD',\n",
       " 'The Howling - Widescreen Subtitle Dolby Special - DVD',\n",
       " 'Bill Burr: Let it Go - Widescreen AC3 Dolby - DVD',\n",
       " 'Great Jaguar Rescue - Fullscreen Dolby - DVD',\n",
       " 'Eyeshield 21: Collection 1 (2 Disc) - Widescreen Subtitle Dolby - DVD',\n",
       " 'Enough - (Ws Dub Spec Sub Dol) - DVD',\n",
       " 'F*ck - Widescreen AC3 Dolby - DVD',\n",
       " 'Doodlebops Quad (3 Disc) - Fullscreen Dolby - DVD',\n",
       " 'Hedwig and the Angry Inch - Dolby - DVD',\n",
       " 'South of Heaven - Widescreen AC3 Dolby - DVD',\n",
       " 'Elf - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Undeclared: The Complete Series [4 Discs] - AC3 Dolby - DVD',\n",
       " 'Just for Laughs: Over the Edge - Fullscreen Dolby - DVD',\n",
       " 'Elena Undone - Widescreen AC3 Dolby - DVD',\n",
       " 'Day X - Widescreen Dolby - DVD',\n",
       " 'Little Shop of Horrors - Dolby - DVD',\n",
       " 'Phone Booth - Widescreen Dubbed Subtitle Dolby - Blu-ray Disc',\n",
       " 'Doubt - Widescreen AC3 Dolby - DVD',\n",
       " 'The Lord of the Rings: The Two Towers - Widescreen Dolby - DVD',\n",
       " 'The Ultimate Wave: Tahiti 3D - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Ni Hao, Kai-lan: Princess Kai-lan - Fullscreen Dolby - DVD',\n",
       " 'Wishbone - Fullscreen Dubbed Dolby - DVD',\n",
       " '5.1: The Surround Sound Collection [CD & DVD] - CD',\n",
       " 'AC/DC: Let There Be Rock - Fullscreen Subtitle AC3 Dolby - DVD',\n",
       " 'The Right Stuff - Subtitle AC3 Dolby - DVD',\n",
       " 'Lord of War - Widescreen Dolby Dts Special - DVD',\n",
       " 'Scary Movie - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Where the Boys Are - Fullscreen Dolby - DVD',\n",
       " \"Chuck Berry: Hail! Hail! Rock 'n' Roll - Widescreen Dolby Dts - DVD\",\n",
       " 'Best of the Best - Widescreen Dolby - DVD',\n",
       " 'Drift - Fullscreen Dolby - DVD',\n",
       " 'War - Subtitle Dolby Dts - Blu-ray Disc',\n",
       " 'Hardware - Widescreen Dolby Limited Special - DVD',\n",
       " 'Pippi Longstocking - Subtitle Dolby - DVD',\n",
       " 'She Hate Me - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Afraid of the Dark - Fullscreen Dolby - DVD',\n",
       " 'Rhyme and Punishment - Widescreen Dolby - DVD',\n",
       " 'Dreamkeeper - Dolby - DVD',\n",
       " 'Four Brothers - Widescreen Subtitle Dolby Dts - Blu-ray Disc',\n",
       " 'Classic Artists: Jethro Tull - Widescreen - DVD',\n",
       " 'The Ideal Husband - Widescreen Dolby - DVD',\n",
       " 'American Dreamer - Widescreen Dolby - DVD',\n",
       " 'National Geographic: Dinosaurs Decoded - Widescreen AC3 Dolby - DVD',\n",
       " \"Precious: Based on the Novel 'Push' By Sapphire - Widescreen Subtitle AC3 Dolby - DVD\",\n",
       " 'Cop Out - Widescreen Dolby - Blu-ray Disc',\n",
       " 'The Bridge - Dolby - DVD',\n",
       " 'MI-5, Vol. 3 [5 Discs] - AC3 Dolby - DVD',\n",
       " 'WALL-E - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Radio Flyer - Widescreen Dolby - DVD',\n",
       " 'State: The Complete Series [5 Discs] - Fullscreen Dolby - DVD',\n",
       " \"Dolan's Cadillac - AC3 Dolby - Blu-ray Disc\",\n",
       " 'Enemy of the State - Widescreen Dubbed AC3 Dolby - Blu-ray Disc',\n",
       " 'Lucky Number Slevin - Widescreen Dolby Dts - DVD',\n",
       " \"Jeff Dunham's Very Special Christmas Special - Widescreen Dolby - DVD\",\n",
       " 'Pink Floyd: The Wall - Widescreen AC3 Anniversary Dolby - DVD',\n",
       " 'Passion Play - Widescreen AC3 Dolby - DVD',\n",
       " 'Air Bud - Widescreen Dubbed AC3 Dolby Special - DVD',\n",
       " 'Dirty Jobs: Something Fishy - Fullscreen Dolby - DVD',\n",
       " 'Numb - Widescreen Dolby - DVD',\n",
       " 'No Impact Man - Subtitle AC3 Dolby - DVD',\n",
       " 'The Joneses - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'DOA: Dead or Alive - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Gangs of New York - Widescreen AC3 Dolby - DVD',\n",
       " 'I Hope They Serve Beer in Hell - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Wet Hot American Summer - Widescreen Dolby - DVD',\n",
       " 'Cujo - Widescreen Subtitle AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Windy City Heat - Fullscreen Dolby - DVD',\n",
       " \"Michael Jackson'S This Is It - Widescreen Subtitle AC3 Dolby - DVD\",\n",
       " 'Sacrifice - AC3 Dolby - DVD',\n",
       " 'Friday: The Animated Series - Fullscreen Dolby - DVD',\n",
       " 'MARVEL KNIGHTS GIFT SET (5PC) / (WS DOL GIFT) - Widescreen Dolby - DVD',\n",
       " 'The Name of the Rose - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Richard Pryor Collection - Widescreen Dolby - DVD',\n",
       " 'STORM CHASERS/PERFECT DISASTER / (WS DOL) - Widescreen Dolby - DVD',\n",
       " 'The Hills Have Eyes - Widescreen Dolby Dts - DVD',\n",
       " 'Moby Dick - Widescreen AC3 Dolby - DVD',\n",
       " 'Alien Planet - Widescreen AC3 Dolby - DVD',\n",
       " 'Halo Legends - Fullscreen Dubbed AC3 Dolby - DVD',\n",
       " 'The Music Never Stopped - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'I Love You Phillip Morris - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Star Wars: The Clone Wars - Widescreen AC3 Dolby - DVD',\n",
       " \"Soft Cell's Non-Stop Exotic Video Show - Subtitle Dolby - DVD\",\n",
       " 'Yo Gabba Gabba!: Meet My Family - Fullscreen Dolby - DVD',\n",
       " 'Twilight - Widescreen Dolby - DVD',\n",
       " 'Rise: Blood Hunter - Widescreen AC3 Dolby - DVD',\n",
       " 'HD Moods: Blu Ocean - AC3 Dolby - Blu-ray Disc',\n",
       " 'Batman: Gotham Knight - Widescreen AC3 Dolby Special - DVD',\n",
       " 'Ghosts of the Abyss - Widescreen Dolby - DVD',\n",
       " 'Wall-E (2 Disc) (W/Dvd) - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'The Crow - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Forget Me Not - Widescreen AC3 Dolby - DVD',\n",
       " 'Steely Dan: In Concert - Widescreen AC3 Dolby Dts - DVD',\n",
       " 'Enchanted April - Widescreen AC3 Dolby - DVD',\n",
       " 'LeapFrog: Letter Factory - Fullscreen Dolby - DVD',\n",
       " 'Inspector Gadget: The Biggest Caper Ever - Widescreen Dolby - DVD',\n",
       " 'Lovers & Friends: Season 1 - Widescreen Dolby - DVD',\n",
       " 'Pervert! - Widescreen AC3 Dolby - DVD',\n",
       " 'Hardware - Widescreen Dolby - Blu-ray Disc',\n",
       " 'Exit Through the Gift Shop - Widescreen Subtitle AC3 Dolby - DVD',\n",
       " 'Sex and the City 2 - Widescreen Dolby - DVD',\n",
       " '35 & Ticking - Widescreen AC3 Dolby - DVD',\n",
       " 'North by Northwest - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Appleseed - Widescreen Dubbed AC3 Dolby - Blu-ray Disc',\n",
       " 'Nick Cannon: Mr. Showbiz - Widescreen AC3 Dolby - DVD',\n",
       " 'Children of the Corn: Genesis - Widescreen Dolby - DVD',\n",
       " 'Wonder Woman - Widescreen Dubbed AC3 Dolby - DVD',\n",
       " 'Trade In - Dolby - DVD',\n",
       " 'Jingle All the Way - Widescreen Fullscreen Dubbed - DVD',\n",
       " 'Time Bandits - Widescreen AC3 Dolby Dts - Blu-ray Disc',\n",
       " 'Blow - Widescreen AC3 Dolby - Blu-ray Disc',\n",
       " 'Flash Point - Dolby Dts - Blu-ray Disc',\n",
       " 'Bowling For Columbine - Subtitle Dolby - DVD',\n",
       " 'The Wraith - Widescreen Dolby Special - DVD',\n",
       " 'A Player to Be Named Later - Dolby - DVD',\n",
       " '1984 - Widescreen Subtitle AC3 Dolby Dts - DVD',\n",
       " \"The Time Traveler's Wife - Widescreen Dolby - DVD\",\n",
       " 'Baa Baa Black Sheep: Volume 2 (3 Discs) - Fullscreen Dolby - DVD']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters[1]  ##40, 82 ,91   ### 82 and 91 very close to each other need to be combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "bee3bcc7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{ cluster: 24, top_words: ['Nintendo', 'Wii', 'DS', '3DS', 'The', 'and', 'Game', 'DSi', 'Mario', 'Super', 'dreamGEAR', 'LEGO', 'Disney', 'LeapFrog', 'Kit', 'Pack', '3D', 'Bundle', 'Party', 'Edition']}\n",
      "{ cluster: 53, top_words: ['Wireless-N', 'Switch', 'Router', 'Ethernet', 'NETGEAR', 'Adapter', '4-Port', 'Wireless', 'USB', 'D-Link', 'Gigabit', 'Linksys', 'Dual-Band', 'Modem', 'Cisco', 'Belkin', 'Network', '2.0', 'Refurbished', '10/100']}\n",
      "{ cluster: 50, top_words: ['CD', 'The', 'You', 'Me', 'Love', 'All', 'VINYL', 'My', 'Is', 'to', 'Up', 'Of', 'and', 'We', 'It', 'Your', 'This', 'on', '[ECD]', 'In']}\n",
      "{ cluster: 30, top_words: ['CD', '[Digipak]', 'The', '[PA]', '[CD', 'DVD]', '[ECD]', 'and', '20th', 'Century', '[Deluxe', 'Masters', 'Live', 'Best', '[Bonus', 'In', 'Various', 'on', 'to', 'You']}\n",
      "{ cluster: 54, top_words: ['CD', 'The', 'Greatest', 'Hits', '[Remaster]', 'Edition)', '(Bonus', '[Bonus', '(Deluxe', 'Tracks]', 'Tracks)', '(Remastered)', 'Edition]', '[Deluxe', 'DVD]', '[PA]', '[CD', 'Of', 'Various', '(CD+DVD)']}\n",
      "{ cluster: 73, top_words: ['CD', '[CD', 'DVD]', '[PA]', 'The', '[Digipak]', 'Icon', '(CD+DVD)', '[Import]', 'Live', 'to', 'In', '[ECD]', 'It', '[Box]', 'and', 'On', '[Remaster]', 'Home', '[CD/DVD]']}\n",
      "{ cluster: 44, top_words: ['Stainless-Steel', '30\"', 'Built-In', 'Electric', 'Range', 'Oven', 'GE', 'Cooktop', 'Gas', 'Hood', 'Wall', 'KitchenAid', 'Cooker', '36\"', 'Convection', 'Cuisinart', 'Profile', 'Series', 'Convertible', 'Double']}\n",
      "{ cluster: 57, top_words: ['Blu-ray', 'Disc', 'The', '3D', 'Disc)', 'Of', '(2', 'Discs]', 'Widescreen', 'and', '[2', 'DVD', 'Harry', '[Blu-ray]', 'Discs', 'Potter', '(3', 'Pan', 'Scan', 'Blu-ray]']}\n",
      "{ cluster: 16, top_words: ['CD', 'The', 'Best', 'Of', '[ECD]', 'Various', 'Is', 'and', '[Edited]', 'to', 'You', 'No', 'Very', 'To', 'Time', 'Back', '(Edited)', 'In', 'This', 'My']}\n",
      "{ cluster: 28, top_words: ['DVD', 'The', 'Discs]', '[2', 'Widescreen', 'Collection', 'and', 'Disc)', 'Subtitle', 'Of', 'Special', \"Collector's\", 'Movie', '(2', 'Fullscreen', 'Disc', 'Blu-ray', 'Vol.', 'B&W', '(Ws']}\n",
      "{ cluster: 72, top_words: ['Black', 'KitchenAid', 'Maker', 'Cuisinart', 'Stainless-Steel', 'White', 'Coffeemaker', 'Mixer', 'Stand', 'Food', 'Coffee', 'Blender', 'Espresso', 'Keurig', 'Series', '12-Cup', 'Silver', 'Processor', 'Hamilton', 'Red']}\n",
      "{ cluster: 26, top_words: ['DVD', 'The', 'Discs]', 'Vol.', 'Collection', '[2', 'Disc)', 'Widescreen', 'Complete', 'and', 'Fullscreen', 'CD', 'Subtitle', '[4', '(2', 'to', 'Of', 'Dolby', 'Box', 'Blu-ray']}\n",
      "{ cluster: 81, top_words: ['DVD', 'The', 'Discs]', '[2', 'Widescreen', 'Subtitle', 'Sub', '(Ws', 'Dub', 'and', 'Dol)', 'Fullscreen', 'Blu-ray', 'Disc', 'Dolby', 'My', 'to', 'Love', 'You', 'Girl']}\n",
      "{ cluster: 63, top_words: ['Apple&#xAE;', 'Case', 'iPod&#xAE;', 'Black', 'iPhone&#xAE;', 'touch', 'iPad&#xAE;', '4th-Generation', 'Tribeca', 'and', 'Hard', 'Shell', 'Griffin', 'Technology', 'OtterBox', 'Series', 'iPad&#x2122;', 'Belkin', 'White', 'Pink']}\n",
      "{ cluster: 58, top_words: ['Vehicles', 'Scosche', 'Select', 'Kit', 'Installation', 'Harness', 'Wiring', 'or', 'Later', 'and', 'Stereo', 'Toyota', 'GM', 'Speaker', 'Interface', 'Honda', 'Black', 'Double-DIN', 'Ford', 'Pocket']}\n",
      "{ cluster: 75, top_words: ['AC3', 'Subtitle', 'DVD', 'Widescreen', 'Dubbed', 'The', 'Fullscreen', 'Dolby', 'and', 'Blu-ray', 'Disc', 'Man', \"Director's\", 'Anniversary', 'Up', 'New', 'Me', 'You', 'II', 'Twilight']}\n",
      "{ cluster: 35, top_words: ['Vacuum', 'Bagless', 'Upright', 'HEPA', 'Cleaner', 'BISSELL', 'Hoover', 'Steam', 'Dyson', 'Canister', 'and', 'Pet', 'Blue', 'Cordless', 'Black', 'Toothbrush', 'Cleaning', 'White', 'Electrolux', 'Eureka']}\n",
      "{ cluster: 82, top_words: ['DVD', 'Discs]', 'The', 'Box', 'Complete', 'Disc)', 'Series', '[2', 'Subtitle', 'Season', 'Collection', 'Vol.', '(2', 'Disc', '[3', 'Blu-ray', 'Part', 'Fullscreen', 'Dubbed', 'Uncut']}\n",
      "{ cluster: 27, top_words: ['System', 'Wireless', 'Camera', 'Surveillance', 'Lorex', 'Kit', 'Select', 'Digital', 'Scanner', 'Calculator', 'Livescribe', 'Security', 'and', 'Optoma', 'Directed', 'Electronics', 'Light', 'Technology', 'Black', 'Solar']}\n",
      "{ cluster: 25, top_words: ['Subtitle', 'DVD', 'Widescreen', 'Dubbed', 'The', 'Blu-ray', 'Disc', 'Fullscreen', 'Dolby', 'Special', 'and', 'Dts', \"Collector's\", 'Disc)', 'B&W', 'to', 'Movie', 'AC3', 'Last', 'Box']}\n",
      "{ cluster: 84, top_words: ['TVs', 'Flat-Panel', 'to', 'Up', 'Stand', 'TV', 'Mount', 'Most', 'Black', 'Wall', 'BDI', '60\"', 'or', 'Sanus', 'Furniture', '32\"', 'Extends', 'Tube', 'Whalen', '42\"']}\n",
      "{ cluster: 61, top_words: ['DVD', 'The', 'Discs]', 'Widescreen', 'Blu-ray', '[2', 'Disc', 'Fullscreen', 'Collection', 'Halloween', 'Dead', 'Subtitle', 'Disc)', 'Movie', 'Of', 'Night', 'and', 'Special', '[3', '(2']}\n",
      "{ cluster: 48, top_words: ['Subwoofer', '10\"', 'Enclosure', '4-Ohm', '12\"', 'Single-Voice-Coil', 'Kicker', 'Dual-Voice-Coil', 'Atrend', 'Dual', 'Powered', 'Sealed', 'Charcoal', 'Audio', 'Single', 'Series', '8\"', 'Black', 'Ported', '2-Ohm']}\n",
      "{ cluster: 74, top_words: ['Black', 'Stand', 'Chair', 'Storage', 'Desk', 'Leather', 'TheaterSeatStore', 'Home', 'Theater', 'Seating', 'Multimedia', 'Speaker', 'Cabinet', 'Bush', 'Innovations', 'Baseline', 'Atlantic', 'Computer', 'Furniture', 'Office']}\n",
      "{ cluster: 29, top_words: ['DVD', 'Workout', 'Yoga', 'The', 'Treadmill', 'Fitness', 'Pro-Form', 'Fullscreen', 'Dance', 'and', 'Pilates', 'Jillian', 'Exercise', 'Elliptical', 'Body', 'Ultimate', 'Cardio', 'Michaels', 'Beginners', 'Power']}\n",
      "{ cluster: 12, top_words: ['Headphones', 'Earbud', 'Black', 'Over-the-Ear', 'Skullcandy', 'White', 'Sony', 'Headset', 'Stereo', 'Beats', 'Ear', 'Bud', 'Sennheiser', 'JVC', 'Dr.', 'Dre', 'Blue', 'By', 'Philips', 'AKG']}\n",
      "{ cluster: 42, top_words: ['Black', '30\"', 'Electric', 'Range', 'Built-In', 'Oven', 'White', 'Gas', 'GE', 'Heater', 'Cooktop', 'Freestanding', 'Wall', 'Grill', 'Frigidaire', 'Hood', 'Whirlpool', 'Toaster', 'Single', '36\"']}\n",
      "{ cluster: 59, top_words: ['CD', 'The', 'Of', 'and', 'to', 'In', 'World', 'My', 'Life', 'Best', '[PA]', 'Black', 'New', '[Slipcase]', 'Love', 'Live', '[ECD]', 'On', 'Various', 'Soul']}\n",
      "{ cluster: 34, top_words: ['DVD', 'Disc', 'Spindle', 'Player', 'CD', 'Memorex', 'Verbatim', 'DVD&#xB1;RW/CD-RW', 'Drive', 'Portable', 'Refurbished', '16x', 'Case', 'Sony', 'Double-Layer', 'CD/DVD', 'Black', 'Blu-ray', 'External', 'DVD+R']}\n",
      "{ cluster: 18, top_words: ['CD', '[PA]', 'The', 'to', 'Best', 'and', 'Is', 'In', 'It', 'Life', 'Greatest', 'Hits', 'All', 'Vol.', '[Digipak]', 'Black', 'No', 'Me', 'You', '[ECD]']}\n",
      "{ cluster: 56, top_words: ['Remote', 'Controller', 'PlayStation', 'Wireless', 'Xbox', '360', 'Nintendo', 'Universal', 'Black', 'Wii', 'Sony', 'Logitech', 'and', 'Control', 'Pro', 'Mad', 'Catz', 'Microsoft', 'PDP', 'Harmony']}\n",
      "{ cluster: 32, top_words: ['Cu.', 'Ft.', 'White', 'Dryer', 'Washer', 'Capacity', 'Electric', 'Whirlpool', 'Gas', 'Steam', 'LG', 'GE', '9-Cycle', 'Samsung', 'High-Efficiency', 'Maytag', '7.4', '7.3', 'Ultra', '13-Cycle']}\n",
      "{ cluster: 62, top_words: ['Widescreen', 'DVD', 'Blu-ray', 'Disc', 'The', 'Dubbed', 'Fullscreen', 'Special', \"Collector's\", 'Discs]', 'and', 'Disc)', '[2', '(2', 'to', 'Dolby', 'Subtitle', 'AC3', 'Dead', \"Director's\"]}\n",
      "{ cluster: 0, top_words: ['CD', 'The', 'Various', 'Best', 'Of', 'Hits', 'Very', 'Songs', '[PA]', 'Vol.', 'Live', 'Greatest', 'to', 'and', 'Tribute', 'Love', 'Playlist:', 'from', 'at', 'Music']}\n",
      "{ cluster: 49, top_words: ['Metra', 'Vehicles', 'Kit', 'Installation', 'Select', 'Black', 'and', 'Harness', 'Adapter', 'Radio', 'Most', 'Wire', 'Wiring', 'Aftermarket', 'GM', 'Ford', 'Antenna', 'Nissan', 'Dash', '2008']}\n",
      "{ cluster: 5, top_words: ['Air', 'White', 'Filter', 'BTU', 'Frigidaire', 'Conditioner', 'Replacement', 'Humidifier', 'Water', 'Window', 'Purifier', 'Black', 'HEPA', 'and', 'Honeywell', 'Mist', 'Fan', 'Cool', 'Gal.', 'Dehumidifier']}\n",
      "{ cluster: 17, top_words: ['DVD', 'Discs]', 'Season', 'The', 'Complete', 'Fullscreen', '[3', 'Widescreen', '[4', '[6', 'First', 'Second', '[2', '[5', 'Subtitle', 'Seasons', 'Fifth', 'Fourth', 'Third', 'Sixth']}\n",
      "{ cluster: 90, top_words: ['Camcorder', 'HD', 'Flash', 'Memory', 'Black', 'Sony', 'LCD', 'Digital', 'Webcam', 'JVC', 'Video', 'Refurbished', '3\"', 'Red', 'Monitor', 'Silver', 'High-Definition', 'Blue', 'Recorder', 'Touch']}\n",
      "{ cluster: 71, top_words: ['Cable', 'Monster', 'HDMI', 'Adapter', 'Technology', 'Hosa', 'Video', 'A/V', 'Belkin', 'StarTech.com', 'USB', \"6'\", 'Black', 'Extension', 'Apple&#xAE;', 'and', 'Digital', \"10'\", 'Mini', 'Cord']}\n",
      "{ cluster: 22, top_words: ['Microphone', 'USB', 'Audio', 'Mixer', 'Black', 'Digital', 'Behringer', 'DJ', 'System', 'Recorder', 'Controller', 'Studio', 'Sound', 'Interface', 'Software', 'Recording', 'M-Audio', 'Voice', 'Condenser', 'Wireless']}\n",
      "{ cluster: 70, top_words: ['Amplifier', 'Class', 'Bridgeable', 'AB', 'Series', 'Power', '2-Channel', 'MOSFET', 'Guitar', 'Kicker', 'Variable', 'Crossover', 'Behringer', 'Mono', 'Combo', 'Multichannel', 'Bass', 'Marine', 'Stereo', 'Crossovers']}\n",
      "{ cluster: 13, top_words: ['HDTV', 'Class', '1080p', '60Hz', 'LED', 'LCD', '720p', '3D', '120Hz', 'Samsung', 'Smart', 'Plasma', '600Hz', 'Refurbished', '32\"', '46\"', 'LG', 'Toshiba', 'Panasonic', 'Sony']}\n",
      "{ cluster: 6, top_words: ['CD', 'The', 'Various', 'Vol.', 'Collection', 'Music', '[Box]', 'Best', '[PA]', 'Of', '20th', 'Century', 'Piano', 'Box', '[CD', 'DVD]', 'DVD', 'Definitive', 'Masters:', 'Millennium']}\n",
      "{ cluster: 10, top_words: ['Memory', 'Processor', 'Drive', 'Hard', 'Intel&#xAE;', 'Laptop', 'Display', '4GB', 'Core&#x2122;', 'HP', '15.6\"', 'AMD', 'Black', 'Desktop', '500GB', 'Pavilion', '6GB', '1TB', 'II', 'i5']}\n",
      "{ cluster: 7, top_words: ['Mobile', 'Phone', 'Black', '(Unlocked)', 'Samsung', 'No-Contract', '(AT&T)', '(Sprint)', '(Verizon', 'Wireless)', 'LG', 'BlackBerry', 'HTC', '4G', '(T-Mobile)', 'Memory', 'Apple&#xAE;', 'White', 'Motorola', 'Refurbished']}\n",
      "{ cluster: 67, top_words: ['Subtitle', 'Widescreen', 'DVD', 'Dolby', 'AC3', 'Dubbed', 'The', 'Fullscreen', 'Dts', 'Blu-ray', 'Disc', 'UMD', 'Saw', 'Dead', 'One', 'Game', 'State', 'III', 'Man', 'My']}\n",
      "{ cluster: 21, top_words: ['Drive', 'USB', 'Hard', 'External', '2.0', 'Black', 'Flash', 'Western', 'Digital', 'Internal', 'Portable', 'Serial', 'ATA', 'Seagate', '1TB', 'My', '500GB', 'State', 'Solid', '2TB']}\n",
      "{ cluster: 78, top_words: ['DVD', 'WWE:', 'The', 'Fullscreen', 'Ultimate', 'Blu-ray', 'Disc', 'AC3', 'Dolby', 'UFC', 'Greatest', '2010', 'vs.', 'Fighting', 'Discs]', 'Best', 'NFL:', 'Vol.', 'Series', 'TNA']}\n",
      "{ cluster: 83, top_words: ['CD', 'Original', 'Soundtrack', 'The', '[Original', 'Soundtrack]', 'Music', '[PA]', 'Motion', 'Of', 'Picture', '(Score)', 'from', 'Vol.', 'From', 'and', '[ECD]', 'Cast', 'Remastered', 'Glee:']}\n",
      "{ cluster: 20, top_words: ['Home', 'Theater', '3D', 'Receiver', 'Player', 'A/V', 'Blu-ray', 'System', '5.1-Ch.', 'Samsung', 'Wi-Fi', 'Pass', 'Through', '7.1-Ch.', 'DVD', 'Refurbished', 'Sony', 'Wireless', '1000W', 'Built-In']}\n",
      "{ cluster: 60, top_words: ['DVD', 'Live', 'The', 'Disc', 'Blu-ray', 'at', 'Dolby', 'CD', 'and', 'Tour', 'Dts', 'Concert', 'AC3', 'From', 'At', 'In', 'Widescreen', '[CD', 'DVD]', 'Hall']}\n",
      "{ cluster: 39, top_words: ['Case', 'Black', 'Camera', 'Bag', 'Lowepro', 'Logic', 'Backpack', 'Most', 'Digital', 'Cases', 'Gator', 'Carrying', 'Guitars', 'Cameras', 'Sony', 'Portable', 'AW', 'and', 'Manfrotto', 'Sling']}\n",
      "{ cluster: 91, top_words: ['Battery', 'Laptops', 'Lithium-Ion', 'Lenmar', 'Select', 'and', 'Series', 'HP', 'Dell', 'Toshiba', 'Sony', 'VAIO', 'Compaq', 'Acer', 'Netbooks', 'Apple&#xAE;', '6-Cell', 'Lithium-Polymer', 'Lenovo', 'Inspiron']}\n",
      "{ cluster: 2, top_words: ['AC3', 'Widescreen', 'Blu-ray', 'Disc', 'Subtitle', 'Dubbed', 'Dolby', 'The', 'Dts', 'DVD', 'Disc)', '(2', 'Discs]', 'Man', 'Fullscreen', 'and', 'Trilogy', '(W/Dvd)', 'Last', 'Day']}\n",
      "{ cluster: 1, top_words: ['Dolby', 'DVD', 'Widescreen', 'AC3', 'The', 'Fullscreen', 'Blu-ray', 'Disc', 'Dts', 'Subtitle', 'Dubbed', 'Special', 'and', 'Discs]', 'Movie', 'to', 'Disc)', 'Love', 'Vol.', 'Series']}\n",
      "{ cluster: 37, top_words: ['System', 'Phone', 'DECT', '6.0', 'Cordless', 'Expandable', 'Digital', 'Answering', 'Panasonic', 'Corded', 'AT&T', 'Handset', 'Speakerphone', 'Expansion', 'Systems', 'Caller', 'VTech', 'ID', 'Uniden', 'Select']}\n",
      "{ cluster: 51, top_words: ['Monitor', 'Widescreen', 'Black', 'LCD', 'LED', 'Projector', 'Flat-Panel', 'HD', 'DLP', 'Samsung', '23\"', 'Screen', 'Asus', 'Power', 'Acoustik', 'Frame', '27\"', 'Digital', 'Photo', '7\"']}\n",
      "{ cluster: 38, top_words: ['Cooler', 'Power', 'Master', 'Supply', 'CPU', 'Fan', 'Antec', 'ATX', 'Cooling', 'Case', 'Mid-Tower', 'Corsair', 'Series', 'Chassis', 'Elite', '120mm', 'Thermaltake', 'ATX/EPS', 'Laptop', 'Tower']}\n",
      "{ cluster: 66, top_words: ['Mobile', 'Phones', 'Case', 'Black', 'Series', 'HTC', 'ZAGG', 'InvisibleSHIELD', 'OtterBox', 'Samsung', 'Clear', 'BlackBerry', 'Platinum', 'Commuter', 'Motorola', 'DROID', 'EVO', '4G', 'Galaxy', 'Xentris']}\n",
      "{ cluster: 47, top_words: ['Battery', 'Energizer', 'Batteries', 'Rechargeable', 'Lithium-Ion', 'NiMH', 'System', 'DigiPower', 'Back-Up', 'Duracell', 'Pack', 'AA', '(4-Pack)', '(2-Pack)', 'Lenmar', 'Select', 'Lithium', 'APC', 'and', 'Cordless']}\n",
      "{ cluster: 11, top_words: ['Camera', 'Digital', 'Black', '14.0-Megapixel', 'Silver', 'Nikon', 'Canon', 'Olympus', '12.1-Megapixel', '14.1-Megapixel', 'Coolpix', 'PowerShot', 'Red', 'Sony', 'Refurbished', 'Blue', 'Cyber-shot', 'FinePix', 'Panasonic', 'Lens']}\n",
      "{ cluster: 45, top_words: ['DVD', 'The', 'Fullscreen', 'Discs]', 'Widescreen', 'and', 'Dubbed', 'Christmas', '[2', 'Blu-ray', 'Disc', 'Subtitle', 'Dolby', 'Collection', 'Little', 'Disc)', 'Vol.', 'Complete', '[3', 'AC3']}\n",
      "{ cluster: 52, top_words: ['PlayStation', 'The', 'PSP', 'Xbox', 'Edition', 'Guide)', '360', '(Game', 'Hits', 'Greatest', 'Game', '3,', 'Nintendo', 'Bundle', 'Call', '360,', 'Pack', 'War', 'Sony', 'Duty:']}\n",
      "{ cluster: 92, top_words: ['Keyboard', 'Wireless', 'Keys', 'Sony', 'Portable', 'Full-Size', 'VAIO', 'Black', 'Casio', 'and', 'Gaming', '61', 'Touch-Sensitive', 'Keyscaper', 'Logitech', 'Adesso', 'Laptops', 'Skin', 'Series', '88']}\n",
      "{ cluster: 4, top_words: ['Cable', 'AudioQuest', 'Audio', 'Monster', 'HDMI', 'Speaker', 'Black', 'White', 'RCA', \"3.3'\", \"6.6'\", 'In-Wall', 'Subwoofer', 'Stereo', 'Kicker', 'Platinum', 'Power', 'Wire', 'Black/Green', \"10'\"]}\n",
      "{ cluster: 36, top_words: ['GPS', 'Garmin', 'n&#xFC;vi', 'TomTom', 'DVD', 'Mount', 'Select', 'Black', 'and', 'Refurbished', 'Magellan', 'Lowrance', 'Most', 'Chartplotter', 'Arkon', 'Golf', 'XL', 'VIA', 'Card', 'Receivers']}\n",
      "{ cluster: 15, top_words: ['Mouse', 'Wireless', 'Black', 'Optical', 'Gaming', 'Logitech', 'Pad', 'Microsoft', 'Razer', 'Mobile', 'Laser', 'and', 'Edition', 'SteelSeries', 'Silver', 'Blue', '3500', 'Red', 'Keyboard', 'Wild']}\n",
      "{ cluster: 31, top_words: ['Black', 'Light', 'White', 'Lighting', 'Red', 'Silver', 'LED', 'Chauvet', 'DJ', 'American', 'Clock', 'and', 'Iron', 'Conair', 'Blue', 'Wine', 'Mirror', 'Ceramic', 'T-Shirt', 'Gray']}\n",
      "{ cluster: 88, top_words: ['Charger', 'Power', 'Adapter', 'and', 'USB', 'Protector', 'Apple&#xAE;', 'Surge', 'AC', 'Travel', 'Belkin', 'Black', 'Charging', 'Kit', 'Monster', 'Nintendo', 'Sony', 'iPhone&#xAE;', 'DigiPower', 'Inverter']}\n",
      "{ cluster: 64, top_words: ['Season', 'DVD', 'Discs]', 'Widescreen', 'Subtitle', 'Dolby', 'The', 'AC3', 'Complete', 'Dubbed', '[4', '[3', '[6', 'Fullscreen', 'Disc', 'Blu-ray', 'First', '[2', '[5', 'Second']}\n",
      "{ cluster: 77, top_words: ['CD', '[PA]', '[ECD]', 'The', '[Digipak]', 'Of', 'and', 'Vol.', 'In', 'II', 'to', '[DualDisc]', 'DualDisc', 'Best', 'To', 'Pt.', 'Triple', 'Feature', '[EP]', 'CD]']}\n",
      "{ cluster: 33, top_words: ['Widescreen', 'DVD', 'Fullscreen', 'Blu-ray', 'Disc', 'The', 'Dubbed', 'AC3', 'and', 'Dts', 'Special', 'to', 'One', 'Pan', 'Scan', 'You', 'No', 'For', 'Down', 'It']}\n",
      "{ cluster: 80, top_words: ['DVD', 'Discs]', 'The', 'Widescreen', 'Fullscreen', 'Subtitle', 'Vol.', '[3', '[2', 'Best', 'Collection', 'Dolby', 'Comedy', 'and', 'AC3', 'Dubbed', 'Tyler', 'Complete', 'Disc)', 'Disc']}\n",
      "{ cluster: 23, top_words: ['VINYL', '[LP]', 'The', '[PA]', 'Vinyl', 'Disc]', '12-Inch', 'Single', '[12inch', '(Ogv)', 'CD', '[Single]', 'Edition)', '[EP]', 'Of', '(Limited', 'Is', 'Vol.', 'In', 'White']}\n",
      "{ cluster: 9, top_words: ['Apple&#xAE;', 'iPod&#xAE;', 'and', 'Radio', 'Deck', 'Black', 'In-Dash', 'iPhone&#xAE;', 'Dock', '50W', 'Speaker', 'Radio-Ready', 'CD', 'Clock', 'iPod&#xAE;-/Satellite', 'MOSFET', 'Sony', 'System', 'AM/FM', 'DVD']}\n",
      "{ cluster: 14, top_words: ['Ft.', 'Cu.', 'Refrigerator', 'Water', 'and', 'Thru-the-Door', 'Ice', 'Stainless-Steel', 'French', 'Door', 'Side-by-Side', 'White', 'Frigidaire', 'Black', 'Whirlpool', 'GE', 'Top-Mount', 'LG', 'Freezer', 'Samsung']}\n",
      "{ cluster: 41, top_words: ['Windows', 'Mac/Windows', 'The', 'Edition', 'Sims', '4:', 'Version', 'Stone', 'Rosetta', 'Level', 'Game', 'War', \"Collector's\", 'Pack', 'and', 'World', 'II', 'II:', 'Dragon', 'Limited']}\n",
      "{ cluster: 68, top_words: ['DVD', 'Season', 'Discs]', 'Complete', 'The', 'First', '[4', '[2', 'Disc)', '[3', 'One', '[6', 'Fullscreen', 'Blu-ray', 'Disc', 'Second', 'Series', '[5', 'Box', 'Seasons']}\n",
      "{ cluster: 99, top_words: ['CD', 'Karaoke:', 'Various', 'Party', 'Karaoke', 'Tyme', 'Vol.', '[CD+G]', 'DVD', 'Pop', 'Hits', 'Disney', 'Sing', 'Songs', 'and', 'System', 'Hits,', '10', 'Nintendo', 'Wii']}\n",
      "{ cluster: 85, top_words: ['Razor', 'Scooter', 'Electric', 'Kit', 'Sports', 'Norelco', 'Black', 'Huffy', 'Philips', 'Bicycle', 'and', 'Trimmer', 'Pro', 'Bravo', 'Shaver', 'Braun', 'Rechargeable', 'Skateboard', 'Conair', 'Monster']}\n",
      "{ cluster: 86, top_words: ['Dishwasher', '24\"', 'Built-In', 'Tall', 'Tub', 'Stainless-Steel', 'Black', 'White', 'GE', 'Bosch', 'Whirlpool', 'KitchenAid', 'Series', 'Frigidaire', 'Evolution', 'Maytag', 'Ascenta', '18\"', 'Built-in', 'Integra']}\n",
      "{ cluster: 89, top_words: ['Watch', 'Monitor', 'Black', 'Digital', 'Heart', 'Rate', \"Men's\", 'GoFit', 'Casio', 'Sport', 'Pedometer', 'Scale', 'Timex', 'Apple&#xAE;', 'Sports', 'Blood', 'Pressure', 'iPod&#xAE;', 'Omron', 'G-Shock']}\n",
      "{ cluster: 96, top_words: ['Windows', 'Adobe', 'Mac', 'Upgrade', 'Mac/Windows', 'Suite', '2011', 'and', 'Edition', 'Pro', 'Home', '2012', 'Creative', '5.5', 'Premium', 'Microsoft', '2010', 'Student', 'Professional', 'Office']}\n",
      "{ cluster: 97, top_words: ['Headphones', 'Headset', 'Over-the-Ear', 'Sennheiser', 'Earbud', 'Wireless', 'Bluetooth', 'Stereo', 'Microphone', 'Ear', 'Plantronics', 'DJ', 'Gaming', 'Xbox', '360', 'Turtle', 'Beach', 'Force', 'Skullcandy', 'System']}\n",
      "{ cluster: 46, top_words: ['CD', 'de', 'DVD', 'El', 'La', 'Various', 'Exitos', 'De', 'Los', 'Grandes', '[CD', 'DVD]', 'En', 'Vol.', 'el', '[ECD]', 'Vivo', 'Amor', 'del', 'Que']}\n",
      "{ cluster: 40, top_words: ['Guitar', 'Electric', '6-String', 'Full-Size', 'Black', 'Natural', 'Guitars', 'Acoustic', 'Fender&#xAE;', 'Pedal', 'Strings', 'Bass', 'Schecter', 'Acoustic/Electric', 'Series', 'Drum', 'Cordoba', '4-String', 'Nylon', 'Wood']}\n",
      "{ cluster: 79, top_words: ['Card', 'Memory', 'Class', 'Secure', 'Digital', 'High', 'Capacity', '(SDHC)', '8GB', 'Prepaid', 'SanDisk', '10', '16GB', 'Wireless', 'Airtime', 'microSDHC', '4GB', 'PNY', 'Transcend', 'Sony']}\n",
      "{ cluster: 87, top_words: ['Lens', 'Cameras', 'Nikon', 'Digital', 'Canon', 'SLR', 'Zoom', 'Filter', 'Tripod', 'Sony', 'Tiffen', '3D', 'Eyewear', 'Select', 'Telephoto', 'Nikkor', 'Gunnar', 'Series', 'USM', 'Glasses']}\n",
      "{ cluster: 55, top_words: ['Cartridge', 'Printer', 'Ink', 'HP', 'Black', 'Brother', 'Wireless', 'Network-Ready', 'Epson', 'Photo', 'Canon', 'All-In-One', 'Paper', 'Inkjet', 'Laser', 'Lexmark', 'Color', 'Print', 'Kodak', 'Pack']}\n",
      "{ cluster: 65, top_words: ['Battery', 'Lenmar', 'Lithium-Ion', 'Phones', 'Mobile', 'Select', 'Most', 'Cordless', 'Lithium-Polymer', 'LG', 'and', 'Samsung', 'Motorola', 'HTC', 'MP3', 'Players', 'Hydride', 'PDAs', 'Nickel-Metal', 'Sony']}\n",
      "{ cluster: 95, top_words: ['Hal', 'Leonard', 'Music', 'Sheet', 'Guitar', 'DVD', 'The', 'Book', 'and', 'CD', 'Various', 'Acoustic', 'to', 'Play', 'Alfred', 'Rock', 'Piano', 'Instructional', 'Best', 'Pack']}\n",
      "{ cluster: 98, top_words: ['Player', 'MP3', 'Black', 'Apple&#xAE;', 'Tablet', 'Memory', '4GB*', 'White', 'iPod', '8GB*', 'Sony', 'Generation', 'Latest', 'Model)', '16GB', 'SanDisk', 'Blue', 'Wi-Fi', 'and', 'Video']}\n",
      "{ cluster: 94, top_words: ['Battery', 'Lithium-Ion', 'Select', 'Lenmar', 'Digital', 'Cameras', 'Canon', 'Camcorders', 'Rechargeable', 'Nikon', 'Panasonic', 'DigiPower', 'and', 'Sony', 'Kodak', 'Samsung', 'JVC', 'Olympus', 'Pack', 'Nickel-Metal']}\n",
      "{ cluster: 43, top_words: ['(Pair)', 'Speaker', 'Speakers', '2-Way', 'Cones', 'Car', '(Each)', '6-1/2\"', 'Black', 'Polypropylene', 'System', '6\"', '5-1/4\"', '3-Way', 'Coaxial', 'Woofer', 'White', 'Audio', '9\"', '4\"']}\n",
      "{ cluster: 93, top_words: ['Range', 'Self-Cleaning', '30\"', 'Freestanding', 'Stainless-Steel', 'Electric', 'Convection', 'Gas', 'GE', 'Slide-In', 'Double', 'Oven', 'Frigidaire', 'Whirlpool', 'Black', 'White', 'Maytag', 'KitchenAid', 'Gallery', 'Dual']}\n",
      "{ cluster: 3, top_words: ['M-Edge', 'Accessories', 'Jacket', 'Kindle', 'Amazon', 'Keyboard', 'and', 'Black', 'Digital', 'Readers', 'Barnes', 'Noble', 'Kobo', 'GO!', 'NOOK', 'Touch', 'Apple&#xAE;', 'iPad&#xAE;', 'NOOKcolor', 'Red']}\n",
      "{ cluster: 76, top_words: ['Memory', 'Desktop', 'DDR2', 'DIMM', '2GB', 'DDR3', 'Kit', 'Laptop', 'SoDIMM', '2-Pack', '4GB', '1GB', 'Technology', 'Corsair', 'PC2-6400', 'PC2-5300', 'ValueRAM', 'Crucial', 'Kingston', 'Master']}\n",
      "{ cluster: 69, top_words: ['Card', 'PCI', 'Graphics', 'Express', '1GB', 'GeForce', 'GDDR5', '2.0', 'Motherboard', '(Socket', 'ATX', 'GTX', 'Radeon', 'HD', 'NVIDIA', 'PNY', 'Intel&#xAE;', 'DDR3', '1155)', 'GT']}\n",
      "{ cluster: 8, top_words: ['Laptop', 'Case', 'Black', 'Sleeve', 'Backpack', 'Apple&#xAE;', 'MacBook&#xAE;', 'Laptops', 'Targus', 'Incase', 'Most', 'Tablets', 'Stand', 'Acme', 'Made', 'Messenger', 'Netbook', 'Pro', 'and', 'Gray']}\n",
      "{ cluster: 19, top_words: ['Microwave', 'Cu.', 'Ft.', 'Over-the-Range', 'Stainless-Steel', 'Mid-Size', 'Black', 'White', 'GE', '2.0', 'Sharp', 'Whirlpool', 'Full-Size', '1.6', 'Frigidaire', '1.8', '1.7', 'LG', 'Compact', '1.5']}\n"
     ]
    }
   ],
   "source": [
    "import collections, itertools\n",
    "\n",
    "stopwords = ['in', 'for', 'with', 'of', 'the']\n",
    "\n",
    "def top_words(clusters, top_n):\n",
    "    # Loop through the clusters and split each string on space\n",
    "    tokenized = [cluster.split() for cluster in clusters]\n",
    "    \n",
    "    # Flatten the list of lists\n",
    "    flattened = list(itertools.chain.from_iterable(tokenized))\n",
    "\n",
    "    \n",
    "    filtered = [word for word in flattened if len(word) > 1 and word not in stopwords]\n",
    "    \n",
    "    # Return the most common words\n",
    "    return [ word for word, count in collections.Counter(filtered).most_common(top_n) ]\n",
    "\n",
    "cluster_labels = {}\n",
    "\n",
    "for cluster_key in clusters.keys():\n",
    "    print(f\"{{ cluster: {cluster_key}, top_words: {top_words(clusters[cluster_key], 20)}}}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "21b6ef94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=numpy.array(product_embeddings)\n",
    "plt.scatter(data.T[0], data.T[1], c='b', **plot_kwds)\n",
    "frame = plt.gca()\n",
    "frame.axes.get_xaxis().set_visible(False)\n",
    "frame.axes.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1dcd1639",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'KMeans' object has no attribute '__name__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[133], line 16\u001b[0m\n\u001b[1;32m     13\u001b[0m     plt\u001b[38;5;241m.\u001b[39mtext(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m, \u001b[38;5;241m0.7\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClustering took \u001b[39m\u001b[38;5;132;01m{:.2f}\u001b[39;00m\u001b[38;5;124m s\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end_time \u001b[38;5;241m-\u001b[39m start_time), fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n\u001b[1;32m     14\u001b[0m     \u001b[38;5;66;03m#return labels\u001b[39;00m\n\u001b[0;32m---> 16\u001b[0m \u001b[43mplot_clusters\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malgo\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mn_clusters\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[133], line 12\u001b[0m, in \u001b[0;36mplot_clusters\u001b[0;34m(data, algorithm, args, kwds)\u001b[0m\n\u001b[1;32m     10\u001b[0m frame\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mget_xaxis()\u001b[38;5;241m.\u001b[39mset_visible(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m     11\u001b[0m frame\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mget_yaxis()\u001b[38;5;241m.\u001b[39mset_visible(\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 12\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClusters found by \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mstr\u001b[39m(\u001b[43malgorithm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m)), fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m24\u001b[39m)\n\u001b[1;32m     13\u001b[0m plt\u001b[38;5;241m.\u001b[39mtext(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.5\u001b[39m, \u001b[38;5;241m0.7\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClustering took \u001b[39m\u001b[38;5;132;01m{:.2f}\u001b[39;00m\u001b[38;5;124m s\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end_time \u001b[38;5;241m-\u001b[39m start_time), fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'KMeans' object has no attribute '__name__'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#new stuff Trey just moved here:\n",
    "def plot_clusters(data, algorithm, args, kwds):\n",
    "    #start_time = time.time()\n",
    "    #labels = algorithm(*args, **kwds).fit_predict(data)\n",
    "    #end_time = time.time()\n",
    "    palette = sns.color_palette('deep', numpy.unique(labels).max() + 1)\n",
    "    colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]\n",
    "    plt.scatter(data.T[0], data.T[1], c=colors, **plot_kwds)\n",
    "    frame = plt.gca()\n",
    "    frame.axes.get_xaxis().set_visible(False)\n",
    "    frame.axes.get_yaxis().set_visible(False)\n",
    "    plt.title('Clusters found by {}'.format(str(algorithm.__name__)), fontsize=24)\n",
    "    plt.text(-0.5, 0.7, 'Clustering took {:.2f} s'.format(end_time - start_time), fontsize=14)\n",
    "    #return labels\n",
    "    \n",
    "plot_clusters(data, algo, (), {'n_clusters':10})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "798c2c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.cluster import KMeans\n",
    "from sklearn.datasets import make_blobs\n",
    "from itertools import cycle\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "n_clusters = 4\n",
    "X, y_true = make_blobs(n_samples=300, centers=n_clusters,\n",
    "                       cluster_std=0.60, random_state=0)\n",
    "#kmeans = KMeans(n_clusters=n_clusters)\n",
    "#kmeans.fit(X)\n",
    "#y_kmeans = kmeans.predict(X)\n",
    "#centers = kmeans.cluster_centers_\n",
    "\n",
    "# map predictions to label\n",
    "labels = {0: \"Cluster 1\", 1: \"Cluster 2\", 2: \"Cluster 3\", 3: \"Cluster 4\"}\n",
    "\n",
    "colors = cycle(cm.tab10.colors)\n",
    "plt.figure()\n",
    "\n",
    "for i in range(n_clusters):\n",
    "    # plot one cluster for each iteration\n",
    "    color = next(colors)\n",
    "    # find indeces corresponding to cluser i\n",
    "    idx = y_kmeans == i\n",
    "    # plot cluster\n",
    "    plt.scatter(X[idx, 0], X[idx, 1], color=color, s=50, label=labels[i], alpha=0.25)\n",
    "    # plot center\n",
    "    plt.scatter(centers[i, 0], centers[i, 1], edgecolors=\"k\", linewidth=2, color=color, s=200, alpha=1)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "393fc08f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>097360309041</th>\n",
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       "      <th>43396129184</th>\n",
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       "</table>\n",
       "<p>65282 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              label           pid\n",
       "793795300225     21  793795300225\n",
       "25192147623      78   25192147623\n",
       "883717008824     70  883717008824\n",
       "008811194420     83  008811194420\n",
       "016861895624     85  016861895624\n",
       "...             ...           ...\n",
       "056035361548     65  056035361548\n",
       "097360309041     97  097360309041\n",
       "43396129184      39   43396129184\n",
       "086429215300     29  086429215300\n",
       "27242809826      14   27242809826\n",
       "\n",
       "[65282 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas \n",
    "\n",
    "### Create Pandas dataframs for product_clusters\n",
    "product_ids_labels= dict(zip(product_ids, labels))\n",
    "print(type(product_ids_labels))\n",
    "pddf_product_ids_labes=pandas.DataFrame.from_dict(product_ids_labels,orient='index',columns=['label'])\n",
    "pddf_product_ids_labes['pid']=pddf_product_ids_labes.index\n",
    "pddf_product_ids_labes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e25dc0c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56844,\n",
       " 87,\n",
       " 'Western Digital - Scorpio Blue 250GB Internal Serial ATA Hard Drive for Laptops',\n",
       " '718037753072')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx_p=product_ids.index('718037753072')\n",
    "\n",
    "idx_p,labels[idx_p] ,product_names[idx_p],product_ids[idx_p]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a2246038",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clusters[28]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2ebfd6ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    }
   ],
   "source": [
    "### Get the cluster for the query based on prediction of the label\n",
    "def get_query_cluster(query):\n",
    "    q_emb = transformer.encode([query], convert_to_tensor=False)\n",
    "    q_label=algo.predict(q_emb)\n",
    "    return q_label\n",
    "\n",
    "\n",
    "#query=\"'Western Digital - Scorpio Blue 250GB Internal Serial ATA Hard Drive for Laptops Serial ATA interface; quiet performance; 1.5GB/sec data transfer rate'\"\n",
    "#query=\" 'Western Digital - Scorpio Blue 250GB Internal Serial ATA Hard Drive for Laptops'\"\n",
    "query=\"microwave\"\n",
    "l=get_query_cluster(query)\n",
    "print(l)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dee37609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "microwave\n"
     ]
    }
   ],
   "source": [
    "import sentence_transformers\n",
    "import pandas \n",
    "import heapq\n",
    "#res = [test_list.index(i) for i in heapq.nlargest(N, test_list)]\n",
    "## get the top n clusters based on similairty with centers \n",
    "def get_top_labels_centers(query,centers ,N=2):\n",
    "    print(query)\n",
    "    q_emb = transformer.encode([query], convert_to_tensor=False)\n",
    "    similarities = sentence_transformers.util.cos_sim(q_emb, centers)\n",
    "    sim=similarities.tolist()[0]\n",
    "    res = [sim.index(i) for i in heapq.nlargest(N, sim)]\n",
    "    return res\n",
    "#query=\"Western Digital - Scorpio Blue 250GB Internal Serial ATA Hard Drive for Laptops Serial ATA interface; quiet performance; 1.5GB/sec data transfer rate\"\n",
    "query=\"microwave\"\n",
    "res=get_top_labels_centers(query,centers,N=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "64624500",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 35, 62, 34, 60]\n"
     ]
    }
   ],
   "source": [
    "print(res)\n",
    "#print(sim.index(max(sim))) ,sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "94ba0fc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/spark/python/pyspark/sql/pandas/conversion.py:474: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for column, series in pdf.iteritems():\n",
      "/usr/local/spark/python/pyspark/sql/pandas/conversion.py:486: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for column, series in pdf.iteritems():\n"
     ]
    }
   ],
   "source": [
    "sdf_id_labeles=spark.createDataFrame(pddf_product_ids_labes).createOrReplaceTempView('products_clusters')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "228a2235",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load signal data to create user-product vectors \n",
    "signals_collection=\"signals\"\n",
    "signals_opts={\"zkhost\": \"aips-zk\", \"collection\": signals_collection}\n",
    "df_signals = spark.read.format(\"solr\").options(**signals_opts).load()\n",
    "df_signals.createOrReplaceTempView(\"signals\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "05fcc078",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create User-proudct data set for personalization\n",
    "spark.sql(\"\"\"\n",
    "select  distinct user, target as product_id , name  as product_name\n",
    "from signals s join products_samples p on s.target=p.upc\n",
    "\"\"\").createOrReplaceTempView(\"user_product\")\n",
    "\n",
    "df_user_p=spark.sql(\"\"\"select user,product_id, product_name,label from user_product a join \n",
    "products_clusters b on a.product_id = b.pid \"\"\").createOrReplaceTempView(\"user_product_label\")\n",
    "\n",
    "#rows=df_user_p.collect()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "746ff6af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------+--------------------+-----+\n",
      "|   user|  product_id|        product_name|label|\n",
      "+-------+------------+--------------------+-----+\n",
      "|u185401|074000612624|Sharp - 0.8 Cu. F...|    2|\n",
      "|u639817|074000612624|Sharp - 0.8 Cu. F...|    2|\n",
      "| u62612|074000612624|Sharp - 0.8 Cu. F...|    2|\n",
      "|u684071|084157186220|Daewoo - Touch Co...|    2|\n",
      "|u517772|084157186220|Daewoo - Touch Co...|    2|\n",
      "|u282231|048231316187|LG - 1.6 Cu. Ft. ...|    2|\n",
      "|u182234|048231316187|LG - 1.6 Cu. Ft. ...|    2|\n",
      "|u718867|048231316170|LG - 1.6 Cu. Ft. ...|    2|\n",
      "|u719224|883049158211|Whirlpool - 2.0 C...|    2|\n",
      "|u700867|084691224365|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u549702|084691224365|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u684841|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u452146|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u576996|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u317852|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u716615|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u122855|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "|u532651|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "| u25776|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "| u12589|084691208068|GE - 0.7 Cu. Ft. ...|    2|\n",
      "+-------+------------+--------------------+-----+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#spark.sql(\"select * from user_product_label limit 2 \").show()\n",
    "\n",
    "spark.sql(\" select * from user_product_label where lower(product_name) like '%microwave%' \").show(20)\n",
    "\n",
    "## micro u638433 |u638433|751492508542|PNY - 2GB microSD...|   23|\n",
    "## micro u514573|036725560406|Samsung - 1.7 Cu..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9ff6e13f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "microwave\n",
      "query_clusters [2, 35]\n",
      " select * from user_product_label where user = 'u514573'  and  label in (2,35 )\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Row(user='u514573', product_id='036725560406', product_name='Samsung - 1.7 Cu. Ft. Over-the-Range Microwave - Black', label=2)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###### Personalized search approach \n",
    "# 1- given the query , get the top N clsuters for this query\n",
    "\n",
    "\n",
    "query=\"microwave\"\n",
    "\n",
    "#query=\"'Western Digital - Scorpio Blue 250GB Internal Serial ATA Hard Drive for Laptops Serial ATA interface; quiet performance; 1.5GB/sec data transfer rate'\"\n",
    "query_clusters=get_top_labels_centers(query,centers,N=2)\n",
    "print(\"query_clusters\",query_clusters)\n",
    "\n",
    "qcluster= ','.join(str (q) for q in query_clusters)\n",
    "# 2- Given the user, find the elements he interacted with within this query clusters\n",
    "user='u514573'\n",
    "user_product_query = \" select * from user_product_label where user = '\" + user  + \"' \" + \" and  label in (\" + qcluster+ \" )\"\n",
    "\n",
    "print(user_product_query)\n",
    "user_items=spark.sql(user_product_query)#.show(10)\n",
    "\n",
    "user_items.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dd1bb695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row(product_id='036725560406')\n",
      "[-9.44319740e-03 -6.52701035e-02 -1.34956939e-02 -2.14545988e-02\n",
      " -5.98770119e-02 -1.42378844e-02 -5.19651081e-03 -9.71718598e-03\n",
      " -2.83798538e-02  1.41727589e-02  5.09610400e-03  2.85506900e-02\n",
      " -2.08095964e-02  4.21384051e-02 -3.41786747e-03  1.08373113e-01\n",
      "  2.92089139e-03  9.63064004e-03  2.49510892e-02 -1.58963669e-02\n",
      " -5.79855405e-02  2.23972537e-02  7.03050150e-03 -3.40860672e-02\n",
      " -2.07883064e-02  1.95801854e-02  6.41680649e-03 -3.64200808e-02\n",
      " -8.86633061e-03 -2.61159819e-02 -3.12964916e-02  3.19175981e-02\n",
      " -7.48751089e-02  2.49883235e-02  1.58364310e-06 -7.75170252e-02\n",
      "  3.24130170e-02  6.49999885e-04 -2.83145458e-02 -2.83593461e-02\n",
      "  6.75219893e-02  3.07300854e-02 -9.26550291e-03  1.43866567e-02\n",
      " -6.03652885e-03 -3.12762111e-02  3.08148731e-02  7.41422623e-02\n",
      " -6.70519695e-02 -4.02895361e-02  1.73798557e-02 -5.36329187e-02\n",
      " -4.51185799e-04  7.86301028e-03  1.89712550e-02 -3.40750511e-03\n",
      " -1.47183565e-03  5.16033312e-03  2.49691736e-02  7.23949671e-02\n",
      "  2.32366957e-02  5.52374795e-02  7.24076619e-03  3.04336939e-02\n",
      "  1.90919768e-02 -1.97611675e-02 -7.47234896e-02  2.89853271e-02\n",
      " -1.10836811e-02  4.25175093e-02  4.01811302e-02  2.08291002e-02\n",
      "  1.40978787e-02  4.25164364e-02 -3.31029482e-02  2.17480212e-02\n",
      "  5.16880676e-02 -9.61987674e-03 -2.85395253e-02 -5.78329712e-02\n",
      " -1.48919135e-01 -2.42588725e-02  4.08846438e-02 -8.27090535e-03\n",
      "  5.29361852e-02  4.73091267e-02 -1.01116970e-02 -3.21955942e-02\n",
      "  1.11460909e-02 -2.01963224e-02  3.09496094e-02 -6.47148713e-02\n",
      "  2.96002608e-02  2.44084932e-02 -2.72171968e-03  3.79971750e-02\n",
      "  3.33015583e-02 -5.49619012e-02 -2.38109357e-03 -2.55079288e-02\n",
      "  2.40335576e-02  4.72349897e-02  1.74765754e-03 -1.03207375e-03\n",
      " -1.26382271e-02 -2.15914249e-02 -2.61758119e-02 -9.93404910e-02\n",
      " -7.70584643e-02  4.43557762e-02  1.94531307e-02 -3.93221527e-02\n",
      " -1.31758405e-02 -4.36646678e-03  1.60875376e-02  7.11408723e-03\n",
      "  1.69983841e-02  5.16295731e-02  3.73735130e-02  4.00403962e-02\n",
      "  4.00763713e-02  6.01874618e-03  4.16880008e-03  2.38538291e-02\n",
      "  6.81748847e-03 -9.32095479e-03 -3.68880667e-03 -1.12045100e-02\n",
      "  2.80983299e-02  1.52284950e-02 -2.20162794e-03 -9.99004766e-03\n",
      " -3.67961428e-03 -1.12574277e-02  1.85063519e-02  3.75589132e-02\n",
      " -1.89055540e-02  1.42258080e-02  4.39101197e-02  5.26778353e-03\n",
      " -4.28117299e-03  2.58254190e-03  1.69890784e-02 -2.26421673e-02\n",
      " -7.42380023e-02  7.25348815e-02 -1.56283677e-02  1.10804282e-01\n",
      " -3.06003005e-03 -5.79561107e-03  3.23612168e-02 -2.69386638e-02\n",
      "  2.59269401e-02  2.06089318e-02  5.83787076e-02  2.32831817e-02\n",
      " -6.19055517e-02  3.41327600e-02  6.71165390e-03  1.92102753e-02\n",
      "  2.57051326e-02  1.31680525e-03 -5.48514687e-02  1.70824286e-02\n",
      "  1.31674921e-02  4.05699462e-02 -5.93338870e-02 -8.60093918e-04\n",
      " -5.50299585e-02 -2.89183203e-02  2.25162208e-02  8.09621718e-03\n",
      "  2.67634168e-02 -2.63545290e-02 -2.16186289e-02  4.13679406e-02\n",
      " -6.61011040e-02 -2.38364562e-03 -6.18231632e-02  1.06609231e-02\n",
      "  1.45364357e-02 -9.10083801e-02  3.55096534e-02  1.13568641e-02\n",
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      "  2.41368148e-03 -1.40329171e-02  1.35246571e-02  1.80916600e-02\n",
      " -4.57912572e-02 -2.72674821e-02  1.51811019e-02 -4.37333668e-03\n",
      "  1.40954356e-03 -2.76412312e-02 -7.47811869e-02  2.00733123e-03\n",
      " -1.24498168e-02  1.69914439e-02 -1.41699584e-02 -2.33862113e-04\n",
      "  1.84696852e-34  4.00531292e-02  2.23055296e-02  8.10880738e-04\n",
      "  2.81801689e-02 -6.45252764e-02 -7.13211857e-03 -1.34894680e-02\n",
      " -1.33352177e-02  5.39838076e-02 -7.28323013e-02  1.96737912e-03]\n"
     ]
    }
   ],
   "source": [
    "# 3-Get All emb vectors for the user product  and create the user vector ( for now just avg)\n",
    "\n",
    "\n",
    "product_ids_u=user_items.select('product_id').collect()\n",
    "\n",
    "avg=None\n",
    "for p in product_ids_u:\n",
    "    \n",
    "    print(p)\n",
    "    if avg is None:\n",
    "        avg = product_ids_emb[p[0]]\n",
    "    else:\n",
    "         avg= (avg+ product_ids_emb[p[0]]) /2\n",
    "\n",
    "print(avg)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "04a77b7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'upc': '048231317436', 'name': 'LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.4165711}, {'upc': '084691211174', 'name': 'GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black', 'manufacturer': 'GE', 'score': 3.4165711}, {'upc': '48231317436', 'name': 'LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.4165711}, {'upc': '84691211174', 'name': 'GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black', 'manufacturer': 'GE', 'score': 3.4165711}, {'upc': '048231317498', 'name': 'LG - 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.3787842}, {'upc': '48231317498', 'name': 'LG - 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.3787842}, {'upc': '037988910427', 'name': 'Panasonic - NNCD989S Microwave Oven - Stainless-Steel', 'manufacturer': 'Panasonic', 'score': 3.2913396}, {'upc': '37988910427', 'name': 'Panasonic - NNCD989S Microwave Oven - Stainless-Steel', 'manufacturer': 'Panasonic', 'score': 3.2913396}, {'upc': '084691170679', 'name': 'GE - 1.1 Cu. Ft. Mid-Size Microwave - Black', 'manufacturer': 'GE', 'score': 3.2356415}, {'upc': '84691170679', 'name': 'GE - 1.1 Cu. Ft. Mid-Size Microwave - Black', 'manufacturer': 'GE', 'score': 3.2356415}]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div id=\"demo\">\n",
       "        <input style=\"width:50%\" readonly type=\"text\" name=\"q\" value=\"microwave\">\n",
       "        <input readonly type=\"submit\" value=\"Search\">\n",
       "\n",
       "    <div class=\"results\">\n",
       "    \t\n",
       "\n",
       "    </div>\n",
       "</div>\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel | <strong>Manufacturer:</strong> LG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black | <strong>Manufacturer:</strong> GE</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel | <strong>Manufacturer:</strong> LG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black | <strong>Manufacturer:</strong> GE</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> LG - 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel | <strong>Manufacturer:</strong> LG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> LG - 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel | <strong>Manufacturer:</strong> LG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Panasonic - NNCD989S Microwave Oven - Stainless-Steel | <strong>Manufacturer:</strong> Panasonic</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Panasonic - NNCD989S Microwave Oven - Stainless-Steel | <strong>Manufacturer:</strong> Panasonic</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> GE - 1.1 Cu. Ft. Mid-Size Microwave - Black | <strong>Manufacturer:</strong> GE</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> GE - 1.1 Cu. Ft. Mid-Size Microwave - Black | <strong>Manufacturer:</strong> GE</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4- Get all items for the search query using solr index\n",
    "## Get the top items from the query cluster for the user\n",
    "### User search query using solr \n",
    "import sys\n",
    "sys.path.append('../..')\n",
    "from aips import *\n",
    "#query=\"Western Digital - Scorpio Blue 250GB Internal Serial ATA Hard Drive for Laptops Serial ATA interface; quiet performance; 1.5GB/sec data transfer rate\"\n",
    "\n",
    "query=\"microwave\"\n",
    "collection = \"products\"\n",
    "request = {\n",
    "    \"query\": query,\n",
    "    \"fields\": [\"upc\", \"name\", \"manufacturer\", \"score\"],\n",
    "    \"limit\": 10,\n",
    "    \"params\": {\n",
    "      \"qf\": \"name manufacturer long_description\",\n",
    "      \"defType\": \"edismax\",\n",
    "      \"sort\": \"score desc, upc asc\"\n",
    "    }\n",
    "}\n",
    "\n",
    "search_results = requests.post(f\"{SOLR_URL}/{collection}/select\", json=request).json()[\"response\"][\"docs\"]\n",
    "print(search_results)\n",
    "display(HTML(render_search_results(query, search_results)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0e00221e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5939799547195435, 0.7581617832183838, 0.5939799547195435, 0.758161723613739, 0.8021668195724487, 0.8021668195724487, 0.6961016654968262, 0.6961016654968262, 0.8397796154022217, 0.8397796154022217]\n",
      "[8, 8, 4, 4, 1, 3, 6, 6, 0, 0]\n",
      "(8, 9, 4, 5, 1, 3, 6, 7, 0, 2)\n",
      "[{'upc': '084691170679', 'name': 'GE - 1.1 Cu. Ft. Mid-Size Microwave - Black', 'manufacturer': 'GE', 'score': 3.2356415}, {'upc': '84691170679', 'name': 'GE - 1.1 Cu. Ft. Mid-Size Microwave - Black', 'manufacturer': 'GE', 'score': 3.2356415}, {'upc': '048231317498', 'name': 'LG - 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.3787842}, {'upc': '48231317498', 'name': 'LG - 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.3787842}, {'upc': '084691211174', 'name': 'GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black', 'manufacturer': 'GE', 'score': 3.4165711}, {'upc': '84691211174', 'name': 'GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black', 'manufacturer': 'GE', 'score': 3.4165711}, {'upc': '037988910427', 'name': 'Panasonic - NNCD989S Microwave Oven - Stainless-Steel', 'manufacturer': 'Panasonic', 'score': 3.2913396}, {'upc': '37988910427', 'name': 'Panasonic - NNCD989S Microwave Oven - Stainless-Steel', 'manufacturer': 'Panasonic', 'score': 3.2913396}, {'upc': '048231317436', 'name': 'LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.4165711}, {'upc': '48231317436', 'name': 'LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel', 'manufacturer': 'LG', 'score': 3.4165711}]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sentence_transformers/util.py:39: UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor. (Triggered internally at ../torch/csrc/utils/tensor_new.cpp:245.)\n",
      "  b = torch.tensor(b)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div id=\"demo\">\n",
       "        <input style=\"width:50%\" readonly type=\"text\" name=\"q\" value=\"microwave\">\n",
       "        <input readonly type=\"submit\" value=\"Search\">\n",
       "\n",
       "    <div class=\"results\">\n",
       "    \t\n",
       "\n",
       "    </div>\n",
       "</div>\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
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       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> GE - 1.1 Cu. Ft. Mid-Size Microwave - Black | <strong>Manufacturer:</strong> GE</p>\n",
       "\t    \t\t<p> \n",
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       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
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       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
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       "\t    \t\t<p><strong>Name:</strong> LG - 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel | <strong>Manufacturer:</strong> LG</p>\n",
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       "    \t</div>\n",
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       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
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       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black | <strong>Manufacturer:</strong> GE</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
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       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> GE - Profile Advantium 1.7 Cu. Ft. Over-the-Range Microwave - Stainless-Steel/Black | <strong>Manufacturer:</strong> GE</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
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       "\t\t</div>\n",
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       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Panasonic - NNCD989S Microwave Oven - Stainless-Steel | <strong>Manufacturer:</strong> Panasonic</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
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       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> Panasonic - NNCD989S Microwave Oven - Stainless-Steel | <strong>Manufacturer:</strong> Panasonic</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
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       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel | <strong>Manufacturer:</strong> LG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t\n",
       "    \t<div style=\"position: relative; width: 100%; height:auto; overflow: auto;\">\n",
       "\t    \t<div style=\"position: relative; float:left; width: 120px; margin-top:25px\">\n",
       "\t    \t\t<img style=\"width:100px; height: auto; max-height:150px\" src=\"../data/retrotech/images/unavailable.jpg\">\n",
       "\t    \t</div>\n",
       "\t    \t<div style=\"position:relative; float:left; clear:none; width: 80%; height:auto\">\n",
       "\t    \t\t<p><strong>Name:</strong> LG - Trim Kit for Select LG Microwave Ovens - Stainless-Steel | <strong>Manufacturer:</strong> LG</p>\n",
       "\t    \t\t<p> \n",
       "\t    \t\t</p>\n",
       "\t    \t</div>\n",
       "    \t</div>\n",
       "    \t\n",
       "\t\t<div style=\"position:relative; clear:both; content: ' '; display: block; height: 1px; margin-top: 10px; margin-bottom:20px\">\n",
       "\t\t\t<hr style=\"color: gray; width: 95%;\" />\n",
       "\t\t</div>\n",
       "\t\t"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 5- Rank all the results based on the user vector \n",
    "\n",
    "# []\n",
    "\n",
    "from operator import itemgetter\n",
    "\n",
    "# This to solve \"0\" issues on beginging of productID, should be removed later \n",
    "#solr_res_emb=[ product_ids_emb[search_results[x]['upc'][1:]]  if search_results[x]['upc'][0]=='0'else  product_ids_emb[search_results[x]['upc']] for x in range(len(search_results))]\n",
    "\n",
    "solr_res_emb=[ product_ids_emb[search_results[x]['upc']] for x in range(len(search_results))]\n",
    "\n",
    "len(solr_res_emb)\n",
    "\n",
    "\n",
    "similarities = sentence_transformers.util.cos_sim(avg, solr_res_emb)\n",
    "\n",
    "sim=similarities.tolist()[0]\n",
    "\n",
    "print(sim)\n",
    "\n",
    "res = [sim.index(i) for i in heapq.nlargest(len(sim), sim)]\n",
    "print(res)\n",
    "\n",
    "res, values = zip(*sorted(enumerate(sim), key=itemgetter(1),reverse=True))\n",
    "\n",
    "print(res)\n",
    "\n",
    "pres_res= [search_results[i] for i in res]\n",
    "### Print original search query\n",
    "#print(search_results)\n",
    "\n",
    "print(pres_res)\n",
    "#display(HTML(render_search_results(query, search_results)))\n",
    "\n",
    "display(HTML(render_search_results(query, pres_res)))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5d271705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        7.68397078e-02,  3.59999612e-02,  4.64731158e-04,  1.59061085e-02,\n",
       "       -2.53613684e-02,  2.80472945e-04,  1.54179623e-02, -5.13964556e-02,\n",
       "        7.05851894e-03,  4.25117016e-02, -3.68562378e-02, -4.26321789e-33,\n",
       "       -1.35496613e-02,  2.31317636e-02, -3.43500897e-02, -5.11555597e-02,\n",
       "        2.43211128e-02,  6.99791685e-03,  1.16173504e-02, -1.57778822e-02,\n",
       "       -3.68707068e-02, -3.06391511e-02,  4.53165323e-02,  9.44922119e-03,\n",
       "        8.50399141e-04,  5.91106229e-02,  5.30596962e-03, -1.27942686e-03,\n",
       "        3.53725702e-02, -4.72147651e-02,  1.16501022e-02,  2.25731283e-02,\n",
       "       -2.73515377e-02,  1.48336939e-03, -5.10523701e-03,  6.12673424e-02,\n",
       "       -4.35456075e-02, -9.06178076e-03,  4.14725626e-03,  4.78332825e-02,\n",
       "        3.74080278e-02, -1.06476173e-02,  1.59809948e-03, -1.09164678e-02,\n",
       "       -3.26189324e-02,  4.74597141e-02,  2.80078617e-04,  8.30337964e-03,\n",
       "       -5.96904419e-02, -5.64996339e-02,  6.28199102e-03,  2.10896507e-02,\n",
       "       -4.13622893e-03, -8.09472706e-03, -2.64252555e-02, -7.50835333e-03,\n",
       "        2.93341894e-02, -6.15957612e-03,  2.48217769e-02,  3.14922561e-03,\n",
       "        1.45226736e-02,  3.05354483e-02, -3.39225680e-02,  4.18760348e-03,\n",
       "        1.41969984e-02,  7.42315128e-03, -1.97469667e-02,  1.90772079e-02,\n",
       "        3.40953050e-03, -3.21332994e-03,  1.79599598e-02,  1.07633770e-02,\n",
       "       -6.75863549e-02, -2.74058618e-02, -8.02284759e-03, -6.68357238e-02,\n",
       "        3.09797935e-03, -6.63064197e-02, -1.05248243e-02, -2.19007526e-02,\n",
       "        1.55919204e-02, -1.01924995e-02, -1.87181383e-02, -1.29448976e-02,\n",
       "        3.29947881e-02, -8.95345137e-02, -6.56565726e-02, -6.97614253e-02,\n",
       "       -1.87346246e-02,  2.00120229e-02,  8.91025513e-02, -1.59462332e-03,\n",
       "       -7.11115589e-03,  2.12794077e-03, -2.88173743e-02, -3.59481201e-02,\n",
       "       -6.70324713e-02, -1.28017783e-01, -2.39901002e-02,  2.77171228e-02,\n",
       "        1.28830234e-02,  4.24352437e-02,  5.99703453e-02, -2.17596032e-02,\n",
       "        2.08489541e-02,  2.80669872e-02,  7.10442215e-02,  2.67110653e-02,\n",
       "       -2.98201460e-02,  3.95402387e-02,  2.70908959e-02,  1.10070314e-02,\n",
       "       -1.45655293e-02,  2.39782147e-02, -2.37091444e-04,  6.72576076e-04,\n",
       "       -1.82011370e-02,  1.28900185e-02,  3.66573445e-02, -3.69877592e-02,\n",
       "       -1.02311745e-02,  2.58675311e-02, -4.98938113e-02, -6.10416848e-03,\n",
       "       -1.23896003e-02, -4.49256822e-02, -4.75345179e-02, -1.08255995e-02,\n",
       "       -2.26134551e-03,  3.30103817e-03, -6.86449651e-03, -2.60916110e-02,\n",
       "       -6.76451484e-03,  1.02243498e-02,  5.01142954e-03,  1.35682439e-02,\n",
       "        8.82971752e-03,  1.99008919e-02, -5.30993566e-03, -7.28289038e-02,\n",
       "        3.21690813e-02,  4.56364900e-02,  1.38009973e-02,  4.03969809e-02,\n",
       "        2.02426364e-07, -6.27766699e-02, -8.13514926e-03, -4.11789492e-02,\n",
       "        1.94127485e-02,  1.26955518e-03,  2.11550426e-02,  2.76228525e-02,\n",
       "       -6.83953390e-02,  2.50613224e-02,  5.39029762e-02, -1.04892370e-03,\n",
       "       -5.58758341e-03, -1.80859510e-02,  9.26931296e-03, -3.42525430e-02,\n",
       "       -5.33485366e-03,  8.49510357e-02, -3.84329073e-02, -1.97150484e-02,\n",
       "       -1.46026434e-02,  2.43312884e-02,  1.80473737e-02, -9.11869295e-03,\n",
       "        2.81332843e-02, -7.36320252e-03, -4.70431745e-02, -1.72633659e-02,\n",
       "       -5.29329525e-03,  3.05275582e-02, -1.63382404e-02,  4.65107672e-02,\n",
       "        6.16705147e-05, -2.21287906e-02,  3.80741432e-02, -2.59100081e-04,\n",
       "       -3.11807264e-02,  5.61528020e-02, -4.76104133e-02, -2.90458160e-03,\n",
       "       -8.39045551e-03,  2.42587198e-02, -1.96033786e-03,  1.83986337e-03,\n",
       "       -4.02967893e-02,  1.49826854e-02,  2.56323256e-03, -3.93239670e-02,\n",
       "       -7.86022022e-02, -6.76172897e-02, -1.19664194e-02,  1.69990957e-02,\n",
       "        4.72340547e-02,  9.88583639e-03,  4.60121548e-03,  2.24600136e-02,\n",
       "        4.61183228e-02,  2.21148431e-02, -1.77181754e-02, -4.81249066e-03,\n",
       "        8.52210447e-02,  2.33898517e-02, -4.21342552e-02, -1.51202790e-02,\n",
       "        3.10528893e-02,  5.68273701e-02, -9.54761133e-02, -4.78979386e-02,\n",
       "        1.91621765e-34, -3.56830731e-02, -1.76246918e-03,  2.74811015e-02,\n",
       "        9.54191107e-03,  4.13917825e-02,  1.71461143e-02,  3.27169560e-02,\n",
       "        3.00719719e-02,  4.26984169e-02, -6.96881786e-02,  3.42020812e-03],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_ids_emb['012505525582'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "646b21ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#clusters[70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6e510c06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+------------+--------------------+--------------------+------------+\n",
      "|                  id|     long_description|manufacturer|                name|    short_description|         upc|\n",
      "+--------------------+--------------------+------------+--------------------+--------------------+------------+\n",
      "|17d07583-8192-42f...|Stainless steel w...|Smart Choice|Smart Choice - St...|6 ft. Stainless S...| 12505525582|\n",
      "|0894a881-371b-4de...|Stainless steel w...|Smart Choice|Smart Choice - St...|6 ft. Stainless S...|012505525582|\n",
      "+--------------------+--------------------+------------+--------------------+--------------------+------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"select * from products where  upc=012505525582   \"\"\").show(20) # where product_id=084691208068 lower(name) like '%mircowave%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "568b1ab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+------------+--------------------+--------------------+\n",
      "|                name|         upc|    short_description|           name_desc|\n",
      "+--------------------+------------+--------------------+--------------------+\n",
      "|GE - 0.7 Cu. Ft. ...|084691208068|10 power levels; ...|GE - 0.7 Cu. Ft. ...|\n",
      "+--------------------+------------+--------------------+--------------------+\n",
      "only showing top 1 row\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"select * from products_samples where  upc=084691208068   \"\"\").show(1) # where product_id=084691208068 lower(name) like '%mircowave%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3f926e14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------+--------------------+\n",
      "|   user|  product_id|        product_name|\n",
      "+-------+------------+--------------------+\n",
      "|u684841|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "|u452146|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "|u576996|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "|u317852|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "|u716615|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "|u122855|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "|u532651|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "| u25776|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "| u12589|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "|u372905|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "+-------+------------+--------------------+\n",
      "only showing top 10 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"select * from user_product where product_id=84691208068 \"\"\").show(10) # where product_id=084691208068 lower(name) like '%mircowave%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3a183770",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------+------------+--------------------+\n",
      "|   user|  product_id|        product_name|\n",
      "+-------+------------+--------------------+\n",
      "|u254574|084691208068|GE - 0.7 Cu. Ft. ...|\n",
      "+-------+------------+--------------------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"select * from user_product where user = 'u254574' \"\"\").show(10) # where product_id=084691208068 lower(name) like '%mircowave%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "631efb42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+-----------+--------------------+------------+-----------+-------+--------------------+------------+--------------------+--------------------+\n",
      "|                  id|   query_id|         signal_time|      target|       type|   user|                name|         upc|    short_description|           name_desc|\n",
      "+--------------------+-----------+--------------------+------------+-----------+-------+--------------------+------------+--------------------+--------------------+\n",
      "|229bd040-c43d-4ea...|u684841_1_2|2020-04-10 04:44:...|084691208068|add-to-cart|u684841|GE - 0.7 Cu. Ft. ...|084691208068|10 power levels; ...|GE - 0.7 Cu. Ft. ...|\n",
      "|4615d200-f05c-478...|u452146_4_8|2020-05-07 10:09:...|084691208068|      click|u452146|GE - 0.7 Cu. Ft. ...|084691208068|10 power levels; ...|GE - 0.7 Cu. Ft. ...|\n",
      "+--------------------+-----------+--------------------+------------+-----------+-------+--------------------+------------+--------------------+--------------------+\n",
      "only showing top 2 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "spark.sql(\"\"\"\n",
    "select  *\n",
    "from signals s join products_samples p  on target=upc where upc=084691208068 and target=084691208068\n",
    "\"\"\").show(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2adaad5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5d2f2496",
   "metadata": {},
   "outputs": [],
   "source": [
    "#####################OLD FAISS and other index to be removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "662d4b19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68421, 768)\n",
      "True\n",
      "index.ntotal 68421\n"
     ]
    }
   ],
   "source": [
    "##FAISS INDEX\n",
    "### Build the vector index  Using FAISS\n",
    "d=len(product_embeddings[0])\n",
    "index = faiss.IndexFlatL2(d)   # build the index\n",
    "\n",
    "#xb = numpy.random.random((len(product_embeddings), d)).astype('float32')\n",
    "xb=numpy.array(product_embeddings)\n",
    "print(xb.shape)\n",
    "print(index.is_trained)\n",
    "index.add(xb)                  # add vectors to the index\n",
    "print(\"index.ntotal\", index.ntotal)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1d4bab19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q:   Sanus - Ultimate Foundations 22\" Speaker Stands (Pair)\n",
      "----> Res: 0 Sanus - Ultimate Foundations 22\" Speaker Stands (Pair) ,  id:  793795300225\n",
      "----> Res: 1 Sanus - Natural 18\" Speaker Stands (Pair) ,  id:  793795281814\n",
      "Q:   Dr Seuss The Cat In The Hat (2003) - (Ws Dub Sub) - DVD\n",
      "----> Res: 0 Dr Seuss The Cat In The Hat (2003) - (Ws Dub Sub) - DVD ,  id:  25192147623\n",
      "----> Res: 1 Dr Seuss The Cat In The Hat (2003) - (Ws Dub Sub) - DVD ,  id:  025192147623\n",
      "Q:   Baby Face - CD\n",
      "----> Res: 0 Baby Face - CD ,  id:  883717008824\n",
      "----> Res: 1 Baby Geniuses - CD ,  id:  788037060128\n"
     ]
    }
   ],
   "source": [
    "#### Test the index \n",
    "k = 2                          # we want to see 4 nearest neighbors\n",
    "qk= 3 ## check for first qk vetcore\n",
    "\n",
    "D, I = index.search(xb[:qk], k) # sanity check\n",
    "'''\n",
    "print(\"I\", I ,type(I),I[0][0])\n",
    "print(\"len-p-names\",len(product_names))\n",
    "print(\"len-p-names\",len(xb))\n",
    "print(\"shape xp\",xb.shape)\n",
    "'''\n",
    "\n",
    "for n in range(qk):\n",
    "    print( \"Q:  \",product_names[n])\n",
    "    for i in range(k):\n",
    "        #print(\"i, n, k,  I[n][i] , I[n]\",i,n,k, I[n][i],I[n] )\n",
    "        #print( \" Q Names \",product_names[n], \"----> I name\", product_names[int(I[n][i])], \" ---> I id \",product_ids[int(I[n][i])] )\n",
    "        print( \"----> Res:\" , i ,  product_names[int(I[n][i])], \",  id: \",product_ids[int(I[n][i])] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "04f61d75",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'user_emb' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[50], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m                       \u001b[38;5;66;03m# we want to see k nearest neighbors\u001b[39;00m\n\u001b[1;32m      5\u001b[0m qk\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;66;03m## check for first qk vetcore\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43muser_emb\u001b[49m[rows[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m### Get user embedding vector.\u001b[39;00m\n\u001b[1;32m     10\u001b[0m uq\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([user_emb[rows[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m]]])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'user_emb' is not defined"
     ]
    }
   ],
   "source": [
    "### find product based on user vector  \" FASISS\"\n",
    "\n",
    "#### seacrh for some vectors\n",
    "k = 3                       # we want to see k nearest neighbors\n",
    "qk= 1 ## check for first qk vetcore\n",
    "\n",
    "print(user_emb[rows[1][0]].shape)\n",
    "### Get user embedding vector.\n",
    "\n",
    "uq=numpy.array([user_emb[rows[1][0]]])\n",
    "### create vector \n",
    "print(uq.shape)\n",
    "\n",
    "D, I = index.search(uq,k)\n",
    "print(\"I\", I ,type(I),I[0][0])\n",
    "print(\"len-p-names\",len(product_texts))\n",
    "print(\"len-p-names\",len(xb))\n",
    "print(\"shape xp\",xb.shape)\n",
    "\n",
    "for n in range(qk):\n",
    "    #print( \"Q:  \",product_names[n])\n",
    "    \n",
    "    for i in range(k):\n",
    "        \n",
    "        #print(\"i, n, k,  I[n][i] , I[n]\",i,n,k, I[n][i],I[n] )\n",
    "        #print( \" Q Names \",product_names[n], \"----> I name\", product_names[int(I[n][i])], \" ---> I id \",product_ids[int(I[n][i])] )\n",
    "        print( \"----> Res:\" , i ,  product_names[int(I[n][i])], \",  id: \",product_ids[int(I[n][i])] )\n",
    "        \n",
    "        \n",
    "    print(\"User-- actual product interaction\")\n",
    "    \n",
    "    query= \"select * from  user_product where user = '\" + str(rows[1][0]) + \"'\"\n",
    "    print(query)\n",
    "    spark.sql(query).show(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "09456e78",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### check the normal query against product \n",
    "### and after personalize it using the user product \n",
    "#print(product_names)\n",
    "\n",
    "q=[\" 2GB microSD Memory Card \"]\n",
    "q=[\"Digital Camera \"]\n",
    "#q=['CD CD CD']\n",
    "q_emb = .encode(q, convert_to_tensor=False)\n",
    "#print(q_emb.shape)\n",
    "#print(product_names.index(\"Watch The Throne (Bonus Tracks) (Deluxe Edition) - CD\"))\n",
    "#print(product_names[product_names.index(\"Watch The Throne (Bonus Tracks) (Deluxe Edition) - CD\")])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a334ab00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.60077155 0.60077155 0.6767436 ]] [[14917 47490 32222]]\n",
      "1\n",
      "int(I[n][i] 14917\n",
      "----> Res: 0 Sony - Cyber-shot 16.2-Megapixel Digital Camera ,  id:  027242808676\n",
      "int(I[n][i] 47490\n",
      "----> Res: 1 Sony - Cyber-shot 16.2-Megapixel Digital Camera ,  id:  27242808676\n",
      "int(I[n][i] 32222\n",
      "----> Res: 2 Sony - Cyber-shot 16.1-Megapixel Zoom Digital Camera - Silver ,  id:  27242808621\n"
     ]
    }
   ],
   "source": [
    "#### check search query\n",
    "k = 3                          # we want to see 4 nearest neighbors\n",
    "qk= 1\n",
    "#print(uq)\n",
    "#print(q_embeddings)\n",
    "\n",
    "D, I = index.search(q_emb,k)\n",
    "print(D,I)\n",
    "print(qk)\n",
    "for n in range(qk):\n",
    "    for i in range(k):\n",
    "        print(\"int(I[n][i]\",int(I[n][i]))\n",
    "        print( \"----> Res:\" , i ,  product_names[int(I[n][i])], \",  id: \",product_ids[int(I[n][i])] )\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "a7173aae",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'user_emb' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[53], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m### PERSONALIZATION\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m uq\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([\u001b[43muser_emb\u001b[49m[rows[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m]]])\n\u001b[1;32m      4\u001b[0m avg_q\u001b[38;5;241m=\u001b[39m (uq \u001b[38;5;241m+\u001b[39m q_emb ) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m\n\u001b[1;32m      6\u001b[0m k \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m                          \u001b[38;5;66;03m# we want to see 4 nearest neighbors\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'user_emb' is not defined"
     ]
    }
   ],
   "source": [
    "### PERSONALIZATION\n",
    "\n",
    "uq=numpy.array([user_emb[rows[1][0]]])\n",
    "avg_q= (uq + q_emb ) /2\n",
    "\n",
    "k = 5                          # we want to see 4 nearest neighbors\n",
    "qk= 1\n",
    "#print(uq)\n",
    "#print(q_embeddings)\n",
    "print(\" Use the original query\")\n",
    "D, I = index.search(q_emb,k)\n",
    "print(D,I)\n",
    "for n in range(qk):\n",
    "    for i in range(k):\n",
    "        print( \"----> Res:\" , i ,  product_names[int(I[n][i])], \",  id: \",product_ids[int(I[n][i])] )\n",
    "        \n",
    "print(\" Use the personalize query  Queryvector + User vector\")\n",
    "\n",
    "D, I = index.search(avg_q,k)\n",
    "print(D,I)\n",
    "for n in range(qk):\n",
    "    for i in range(k):\n",
    "        print( \"----> Res:\" , i ,  product_names[int(I[n][i])], \",  id: \",product_ids[int(I[n][i])] )\n",
    "      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6af24e4a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5394b046",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sony - Cyber-shot 16.2-Megapixel Digital Camera', 'Sony - Cyber-shot 16.2-Megapixel Digital Camera']\n"
     ]
    }
   ],
   "source": [
    "#### USING NMSLIB instead of FAISS\n",
    "\n",
    "import nmslib\n",
    "# initialize a new index, using a HNSW index on Cosine Similarity\n",
    "nmsIndex = nmslib.init(method='hnsw', space='cosinesimil')\n",
    "nmsIndex.addDataPointBatch(product_embeddings)\n",
    "nmsIndex.createIndex(print_progress=True)\n",
    "\n",
    "# Example query for the new index.  The 25th embedding is the term 'bag'\n",
    "ids, distances = nmsIndex.knnQuery(q_emb, k=2)\n",
    "matches = [product_names[idx] for idx in ids]\n",
    "print(matches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8906956a",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'user_emb' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[55], line 8\u001b[0m\n\u001b[1;32m      3\u001b[0m q_emb \u001b[38;5;241m=\u001b[39m stsb\u001b[38;5;241m.\u001b[39mencode(q, convert_to_tensor\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m## PERSONALIZATION\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m uq\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([\u001b[43muser_emb\u001b[49m[rows[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m0\u001b[39m]]])\n\u001b[1;32m     10\u001b[0m avg_q\u001b[38;5;241m=\u001b[39m ((\u001b[38;5;241m0.5\u001b[39m\u001b[38;5;241m*\u001b[39muq) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1.5\u001b[39m\u001b[38;5;241m*\u001b[39mq_emb )\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m\n\u001b[1;32m     11\u001b[0m avg_q\u001b[38;5;241m=\u001b[39m ((\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39muq) \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\u001b[38;5;241m*\u001b[39mq_emb )\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m2\u001b[39m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'user_emb' is not defined"
     ]
    }
   ],
   "source": [
    "q=[\" 2GB microSD Memory Card \"]\n",
    "q=['Digital camera']\n",
    "q_emb = .encode(q, convert_to_tensor=False)\n",
    "\n",
    "\n",
    "## PERSONALIZATION\n",
    "\n",
    "uq=numpy.array([user_emb[rows[1][0]]])\n",
    "\n",
    "avg_q= ((0.5*uq) + 1.5*q_emb )/2\n",
    "avg_q= ((1*uq) + 1*q_emb )/2\n",
    "\n",
    "k =5                          # we want to see 4 nearest neighbors\n",
    "qk= 1\n",
    "#print(uq)\n",
    "#print(q_embeddings)\n",
    "print(\" Use the original query: Query vector\")\n",
    "I,D = nmsIndex.knnQuery(q_emb, k)\n",
    "print(D,I)\n",
    "\n",
    "for i in I:\n",
    "    print( \"----> Res:\" , i ,  product_names[i], \",  id: \",product_ids[i] )\n",
    "        \n",
    "print(\" Use the personalization query:  Query vector + User vector\")\n",
    "\n",
    "I, D = nmsIndex.knnQuery(avg_q,k)\n",
    "print(D,I)\n",
    "for i in range(k):\n",
    "        print( \"----> Res:\" , i ,  product_names[int(I[i])], \",  id: \",product_ids[int(I[i])] )\n",
    "      \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "374c8777",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Second approach, use the user vector to rank the results\n",
    "import sentence_transformers\n",
    "import pandas\n",
    "\n",
    "#Find simialrity score between the queries and results\n",
    "\n",
    "similarities = sentence_transformers.util.cos_sim(uq, product_embeddings[I])\n",
    "\n",
    "similarities=similarities.tolist()\n",
    "print(similarities)\n",
    "a_phrases = []\n",
    "b_phrases = []\n",
    "scores = []\n",
    "for a in range(len(similarities)):\n",
    "    for b in range(a, len(similarities[a])):\n",
    "        a_phrases.append(product_names[I[a]])\n",
    "        b_phrases.append(product_names[I[b]])\n",
    "        scores.append(float(similarities[a][b]))\n",
    "print(scores)        \n",
    "comparisons = pandas.DataFrame({\"Query\":a_phrases,\"Result\":b_phrases,\"score\":scores,\"name\":\"similarity\"})\n",
    "comparisons = comparisons.sort_values(by=[\"score\"], ascending=False, ignore_index=True)\n",
    "comparisons[\"idx\"] = range(len(comparisons))\n",
    "comparisons[comparisons[\"score\"]>0 ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7243a229",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2fc0fe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=numpy.array(product_embeddings)\n",
    "plt.scatter(data.T[0], data.T[1], c='b', **plot_kwds)\n",
    "frame = plt.gca()\n",
    "frame.axes.get_xaxis().set_visible(False)\n",
    "frame.axes.get_yaxis().set_visible(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "03aba755",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_clusters(data, algorithm, args, kwds):\n",
    "    start_time = time.time()\n",
    "    labels = algorithm(*args, **kwds).fit_predict(data)\n",
    "    end_time = time.time()\n",
    "    palette = sns.color_palette('deep', numpy.unique(labels).max() + 1)\n",
    "    colors = [palette[x] if x >= 0 else (0.0, 0.0, 0.0) for x in labels]\n",
    "    plt.scatter(data.T[0], data.T[1], c=colors, **plot_kwds)\n",
    "    frame = plt.gca()\n",
    "    frame.axes.get_xaxis().set_visible(False)\n",
    "    frame.axes.get_yaxis().set_visible(False)\n",
    "    plt.title('Clusters found by {}'.format(str(algorithm.__name__)), fontsize=24)\n",
    "    plt.text(-0.5, 0.7, 'Clustering took {:.2f} s'.format(end_time - start_time), fontsize=14)\n",
    "    return labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "72857020",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_clusters(data, algorithm, args, kwds):\n",
    "    start_time = time.time()\n",
    "    algo=algorithm(*args, **kwds).fit(data)\n",
    "    labels = algo.predict(data)\n",
    "    end_time = time.time()\n",
    "    return algo, labels,algo.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "a76283bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "8b82c360",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "e9bb0859",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4c362cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d1b3f1df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=[len(clusters[i]) for i in clusters]\n",
    "len(centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "77aebb3c",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'q_emb1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[60], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdouble\u001b[39m\u001b[38;5;124m'\u001b[39m), row)))\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#a=cast_vector(q_emb1)\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m a \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[43mq_emb1\u001b[49m, dtype\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mdouble)\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mtype\u001b[39m(q_emb1),q_emb1\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[1;32m     11\u001b[0m l\u001b[38;5;241m=\u001b[39malgo\u001b[38;5;241m.\u001b[39mpredict(q_emb1)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'q_emb1' is not defined"
     ]
    }
   ],
   "source": [
    "aq_emb1 = .encode([\"Lumix S3 14.1-Megapixel Digital Camera - Black\"], convert_to_tensor=False)\n",
    "\n",
    "def cast_vector(row):\n",
    "    return numpy.array(list(map(lambda x: x.astype('double'), row)))\n",
    "#a=cast_vector(q_emb1)\n",
    "\n",
    "a = numpy.array(q_emb1, dtype=numpy.double)\n",
    "\n",
    "print(type(q_emb1),q_emb1.dtype)\n",
    "\n",
    "l=algo.predict(q_emb1)\n",
    "print(l)\n",
    "#print(clusters[27])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7e2e9f84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels=plot_clusters(data, cluster.KMeans, (), {'n_clusters':100})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9e910d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 10, 34, ...,  6, 47, 53], dtype=int32)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0fdcf7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_clusters(data, cluster.AffinityPropagation, (), {'preference':-5.0, 'damping':0.95})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "ed762948",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfb76596ee9c416ebe82c0025bdd62f3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/548M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import GPT2Tokenizer, GPT2Model\n",
    "tokenizer = GPT2Tokenizer.from_pretrained('gpt2')\n",
    "model = GPT2Model.from_pretrained('gpt2')\n",
    "text = \"Replace me by any text you'd like.\"\n",
    "encoded_input = tokenizer(text, return_tensors='pt')\n",
    "output = model(**encoded_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "fab3ec35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "last_hidden_states = output.last_hidden_state\n",
    "arr=last_hidden_states.detach().numpy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "502af44c",
   "metadata": {},
   "outputs": [
    {
     "ename": "AuthenticationError",
     "evalue": "No API key provided. You can set your API key in code using 'openai.api_key = <API-KEY>', or you can set the environment variable OPENAI_API_KEY=<API-KEY>). If your API key is stored in a file, you can point the openai module at it with 'openai.api_key_path = <PATH>'. You can generate API keys in the OpenAI web interface. See https://onboard.openai.com for details, or email support@openai.com if you have any questions.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAuthenticationError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[159], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mopenai\u001b[39;00m\u001b[38;5;241m,\u001b[39m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[43mopenai\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEmbedding\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfeline friends say\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmeow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-similarity-davinci-001\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m embedding_a \u001b[38;5;241m=\u001b[39m resp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124membedding\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m      8\u001b[0m embedding_b \u001b[38;5;241m=\u001b[39m resp[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m1\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124membedding\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/openai/api_resources/embedding.py:33\u001b[0m, in \u001b[0;36mEmbedding.create\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 33\u001b[0m         response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     35\u001b[0m         \u001b[38;5;66;03m# If a user specifies base64, we'll just return the encoded string.\u001b[39;00m\n\u001b[1;32m     36\u001b[0m         \u001b[38;5;66;03m# This is only for the default case.\u001b[39;00m\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m user_provided_encoding_format:\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py:149\u001b[0m, in \u001b[0;36mEngineAPIResource.create\u001b[0;34m(cls, api_key, api_base, api_type, request_id, api_version, organization, **params)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate\u001b[39m(\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28mcls\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams,\n\u001b[1;32m    137\u001b[0m ):\n\u001b[1;32m    138\u001b[0m     (\n\u001b[1;32m    139\u001b[0m         deployment_id,\n\u001b[1;32m    140\u001b[0m         engine,\n\u001b[1;32m    141\u001b[0m         timeout,\n\u001b[1;32m    142\u001b[0m         stream,\n\u001b[1;32m    143\u001b[0m         headers,\n\u001b[1;32m    144\u001b[0m         request_timeout,\n\u001b[1;32m    145\u001b[0m         typed_api_type,\n\u001b[1;32m    146\u001b[0m         requestor,\n\u001b[1;32m    147\u001b[0m         url,\n\u001b[1;32m    148\u001b[0m         params,\n\u001b[0;32m--> 149\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__prepare_create_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    150\u001b[0m \u001b[43m        \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapi_base\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapi_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mapi_version\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morganization\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\n\u001b[1;32m    151\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    153\u001b[0m     response, _, api_key \u001b[38;5;241m=\u001b[39m requestor\u001b[38;5;241m.\u001b[39mrequest(\n\u001b[1;32m    154\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    155\u001b[0m         url,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    160\u001b[0m         request_timeout\u001b[38;5;241m=\u001b[39mrequest_timeout,\n\u001b[1;32m    161\u001b[0m     )\n\u001b[1;32m    163\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m stream:\n\u001b[1;32m    164\u001b[0m         \u001b[38;5;66;03m# must be an iterator\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/openai/api_resources/abstract/engine_api_resource.py:106\u001b[0m, in \u001b[0;36mEngineAPIResource.__prepare_create_request\u001b[0;34m(cls, api_key, api_base, api_type, api_version, organization, **params)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    104\u001b[0m     params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m MAX_TIMEOUT\n\u001b[0;32m--> 106\u001b[0m requestor \u001b[38;5;241m=\u001b[39m \u001b[43mapi_requestor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mAPIRequestor\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    107\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapi_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    108\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapi_base\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_base\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    109\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapi_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    110\u001b[0m \u001b[43m    \u001b[49m\u001b[43mapi_version\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mapi_version\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    111\u001b[0m \u001b[43m    \u001b[49m\u001b[43morganization\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morganization\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    112\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    113\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39mclass_url(engine, api_type, api_version)\n\u001b[1;32m    114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m    115\u001b[0m     deployment_id,\n\u001b[1;32m    116\u001b[0m     engine,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    124\u001b[0m     params,\n\u001b[1;32m    125\u001b[0m )\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/openai/api_requestor.py:130\u001b[0m, in \u001b[0;36mAPIRequestor.__init__\u001b[0;34m(self, key, api_base, api_type, api_version, organization)\u001b[0m\n\u001b[1;32m    121\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m    122\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    123\u001b[0m     key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    127\u001b[0m     organization\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    128\u001b[0m ):\n\u001b[1;32m    129\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapi_base \u001b[38;5;241m=\u001b[39m api_base \u001b[38;5;129;01mor\u001b[39;00m openai\u001b[38;5;241m.\u001b[39mapi_base\n\u001b[0;32m--> 130\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapi_key \u001b[38;5;241m=\u001b[39m key \u001b[38;5;129;01mor\u001b[39;00m \u001b[43mutil\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_api_key\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapi_type \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    132\u001b[0m         ApiType\u001b[38;5;241m.\u001b[39mfrom_str(api_type)\n\u001b[1;32m    133\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m api_type\n\u001b[1;32m    134\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m ApiType\u001b[38;5;241m.\u001b[39mfrom_str(openai\u001b[38;5;241m.\u001b[39mapi_type)\n\u001b[1;32m    135\u001b[0m     )\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapi_version \u001b[38;5;241m=\u001b[39m api_version \u001b[38;5;129;01mor\u001b[39;00m openai\u001b[38;5;241m.\u001b[39mapi_version\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/openai/util.py:186\u001b[0m, in \u001b[0;36mdefault_api_key\u001b[0;34m()\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m openai\u001b[38;5;241m.\u001b[39mapi_key\n\u001b[1;32m    185\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 186\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m openai\u001b[38;5;241m.\u001b[39merror\u001b[38;5;241m.\u001b[39mAuthenticationError(\n\u001b[1;32m    187\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo API key provided. You can set your API key in code using \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopenai.api_key = <API-KEY>\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, or you can set the environment variable OPENAI_API_KEY=<API-KEY>). If your API key is stored in a file, you can point the openai module at it with \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mopenai.api_key_path = <PATH>\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. You can generate API keys in the OpenAI web interface. See https://onboard.openai.com for details, or email support@openai.com if you have any questions.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    188\u001b[0m     )\n",
      "\u001b[0;31mAuthenticationError\u001b[0m: No API key provided. You can set your API key in code using 'openai.api_key = <API-KEY>', or you can set the environment variable OPENAI_API_KEY=<API-KEY>). If your API key is stored in a file, you can point the openai module at it with 'openai.api_key_path = <PATH>'. You can generate API keys in the OpenAI web interface. See https://onboard.openai.com for details, or email support@openai.com if you have any questions."
     ]
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "4ec5425a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.evaluation import RegressionEvaluator\n",
    "from pyspark.ml.recommendation import ALS\n",
    "from pyspark.ml.feature import StringIndexer, IndexToString\n",
    "from pyspark.sql.functions import col, explode\n",
    "\n",
    "signals_collection = \"signals\"\n",
    "\n",
    "# Load signals from Solr\n",
    "signals_read_opts = {\"zkhost\": \"aips-zk\", \"collection\": signals_collection}\n",
    "sql_filter = \"\"\"select user, target, type\n",
    "from signals\n",
    "where user is not null and type='click'\n",
    "\"\"\"\n",
    "signals_df = spark.read.format(\"solr\").options(**signals_read_opts).load()\n",
    "signals_df.registerTempTable(\"signals\")\n",
    "signals_df = spark.sql(sql_filter)\n",
    "\n",
    "# Encode 'user' and 'target' fields into integer IDs\n",
    "indexer_user = StringIndexer(inputCol=\"user\", outputCol=\"userId\")\n",
    "indexer_target = StringIndexer(inputCol=\"target\", outputCol=\"itemId\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5ad6a34c",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_model_user = indexer_user.fit(signals_df)\n",
    "index_model_target = indexer_target.fit(signals_df)\n",
    "signals_df = index_model_user.transform(signals_df)\n",
    "signals_df = index_model_target.transform(signals_df)\n",
    "\n",
    "# Prepare data for ALS model\n",
    "data = signals_df.select(col(\"userId\").cast(\"int\"), \n",
    "                         col(\"itemId\").cast(\"int\"), \n",
    "                         col(\"type\").cast(\"int\"))\n",
    "\n",
    "# Split the data into training and test sets\n",
    "(training, test) = data.randomSplit([0.8, 0.2])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "69b86fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize ALS model\n",
    "als = ALS(maxIter=5, regParam=0.01, userCol=\"userId\", itemCol=\"itemId\", ratingCol=\"type\", coldStartStrategy=\"drop\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "9dbc4f66",
   "metadata": {},
   "outputs": [
    {
     "ename": "Py4JJavaError",
     "evalue": "An error occurred while calling o484.fit.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 57.0 failed 1 times, most recent failure: Lost task 0.0 in stage 57.0 (TID 199) (e4cddd0ffcb3 executor driver): java.lang.NullPointerException: Value at index 2 is null\n\tat org.apache.spark.sql.errors.QueryExecutionErrors$.valueIsNullError(QueryExecutionErrors.scala:1733)\n\tat org.apache.spark.sql.Row.getAnyValAs(Row.scala:527)\n\tat org.apache.spark.sql.Row.getFloat(Row.scala:263)\n\tat org.apache.spark.sql.Row.getFloat$(Row.scala:263)\n\tat org.apache.spark.sql.catalyst.expressions.GenericRow.getFloat(rows.scala:166)\n\tat org.apache.spark.ml.recommendation.ALS.$anonfun$fit$2(ALS.scala:713)\n\tat scala.collection.Iterator$$anon$10.next(Iterator.scala:461)\n\tat scala.collection.Iterator$SliceIterator.next(Iterator.scala:273)\n\tat scala.collection.Iterator.foreach(Iterator.scala:943)\n\tat scala.collection.Iterator.foreach$(Iterator.scala:943)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1431)\n\tat scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)\n\tat scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)\n\tat scala.collection.TraversableOnce.to(TraversableOnce.scala:366)\n\tat scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)\n\tat scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)\n\tat scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1431)\n\tat org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1470)\n\tat org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2268)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:136)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)\n\tat java.base/java.lang.Thread.run(Thread.java:833)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2672)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2608)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2607)\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2607)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1182)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1182)\n\tat scala.Option.foreach(Option.scala:407)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1182)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2860)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2802)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2791)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:952)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2228)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2249)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2268)\n\tat org.apache.spark.rdd.RDD.$anonfun$take$1(RDD.scala:1470)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:406)\n\tat org.apache.spark.rdd.RDD.take(RDD.scala:1443)\n\tat org.apache.spark.rdd.RDD.$anonfun$isEmpty$1(RDD.scala:1578)\n\tat scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:406)\n\tat org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1578)\n\tat org.apache.spark.ml.recommendation.ALS$.train(ALS.scala:960)\n\tat org.apache.spark.ml.recommendation.ALS.$anonfun$fit$1(ALS.scala:722)\n\tat org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)\n\tat scala.util.Try$.apply(Try.scala:213)\n\tat org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)\n\tat org.apache.spark.ml.recommendation.ALS.fit(ALS.scala:704)\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)\n\tat java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.base/java.lang.reflect.Method.invoke(Method.java:568)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)\n\tat py4j.ClientServerConnection.run(ClientServerConnection.java:106)\n\tat java.base/java.lang.Thread.run(Thread.java:833)\nCaused by: java.lang.NullPointerException: Value at index 2 is null\n\tat org.apache.spark.sql.errors.QueryExecutionErrors$.valueIsNullError(QueryExecutionErrors.scala:1733)\n\tat org.apache.spark.sql.Row.getAnyValAs(Row.scala:527)\n\tat org.apache.spark.sql.Row.getFloat(Row.scala:263)\n\tat org.apache.spark.sql.Row.getFloat$(Row.scala:263)\n\tat org.apache.spark.sql.catalyst.expressions.GenericRow.getFloat(rows.scala:166)\n\tat org.apache.spark.ml.recommendation.ALS.$anonfun$fit$2(ALS.scala:713)\n\tat scala.collection.Iterator$$anon$10.next(Iterator.scala:461)\n\tat scala.collection.Iterator$SliceIterator.next(Iterator.scala:273)\n\tat scala.collection.Iterator.foreach(Iterator.scala:943)\n\tat scala.collection.Iterator.foreach$(Iterator.scala:943)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1431)\n\tat scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)\n\tat scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)\n\tat scala.collection.TraversableOnce.to(TraversableOnce.scala:366)\n\tat scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)\n\tat scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)\n\tat scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1431)\n\tat org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1470)\n\tat org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2268)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:136)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)\n\t... 1 more\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPy4JJavaError\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[145], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Train ALS model\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mals\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/spark/python/pyspark/ml/base.py:205\u001b[0m, in \u001b[0;36mEstimator.fit\u001b[0;34m(self, dataset, params)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy(params)\u001b[38;5;241m.\u001b[39m_fit(dataset)\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 205\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    206\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    207\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m    208\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mParams must be either a param map or a list/tuple of param maps, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    209\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut got \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mtype\u001b[39m(params)\n\u001b[1;32m    210\u001b[0m     )\n",
      "File \u001b[0;32m/usr/local/spark/python/pyspark/ml/wrapper.py:383\u001b[0m, in \u001b[0;36mJavaEstimator._fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_fit\u001b[39m(\u001b[38;5;28mself\u001b[39m, dataset: DataFrame) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m JM:\n\u001b[0;32m--> 383\u001b[0m     java_model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_java\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    384\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_create_model(java_model)\n\u001b[1;32m    385\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_copyValues(model)\n",
      "File \u001b[0;32m/usr/local/spark/python/pyspark/ml/wrapper.py:380\u001b[0m, in \u001b[0;36mJavaEstimator._fit_java\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m    377\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_java_obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    379\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transfer_params_to_java()\n\u001b[0;32m--> 380\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_java_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_jdf\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/py4j/java_gateway.py:1322\u001b[0m, in \u001b[0;36mJavaMember.__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1316\u001b[0m command \u001b[38;5;241m=\u001b[39m proto\u001b[38;5;241m.\u001b[39mCALL_COMMAND_NAME \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m   1317\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_header \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m   1318\u001b[0m     args_command \u001b[38;5;241m+\u001b[39m\\\n\u001b[1;32m   1319\u001b[0m     proto\u001b[38;5;241m.\u001b[39mEND_COMMAND_PART\n\u001b[1;32m   1321\u001b[0m answer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgateway_client\u001b[38;5;241m.\u001b[39msend_command(command)\n\u001b[0;32m-> 1322\u001b[0m return_value \u001b[38;5;241m=\u001b[39m \u001b[43mget_return_value\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1323\u001b[0m \u001b[43m    \u001b[49m\u001b[43manswer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgateway_client\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1325\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m temp_arg \u001b[38;5;129;01min\u001b[39;00m temp_args:\n\u001b[1;32m   1326\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(temp_arg, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_detach\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "File \u001b[0;32m/usr/local/spark/python/pyspark/sql/utils.py:190\u001b[0m, in \u001b[0;36mcapture_sql_exception.<locals>.deco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdeco\u001b[39m(\u001b[38;5;241m*\u001b[39ma: Any, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: Any) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    189\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    191\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m Py4JJavaError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    192\u001b[0m         converted \u001b[38;5;241m=\u001b[39m convert_exception(e\u001b[38;5;241m.\u001b[39mjava_exception)\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/py4j/protocol.py:326\u001b[0m, in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m    324\u001b[0m value \u001b[38;5;241m=\u001b[39m OUTPUT_CONVERTER[\u001b[38;5;28mtype\u001b[39m](answer[\u001b[38;5;241m2\u001b[39m:], gateway_client)\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m answer[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m==\u001b[39m REFERENCE_TYPE:\n\u001b[0;32m--> 326\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m Py4JJavaError(\n\u001b[1;32m    327\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while calling \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39m\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;28mformat\u001b[39m(target_id, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m, name), value)\n\u001b[1;32m    329\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    330\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m Py4JError(\n\u001b[1;32m    331\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAn error occurred while calling \u001b[39m\u001b[38;5;132;01m{0}\u001b[39;00m\u001b[38;5;132;01m{1}\u001b[39;00m\u001b[38;5;132;01m{2}\u001b[39;00m\u001b[38;5;124m. Trace:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{3}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39m\n\u001b[1;32m    332\u001b[0m         \u001b[38;5;28mformat\u001b[39m(target_id, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m, name, value))\n",
      "\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o484.fit.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 57.0 failed 1 times, most recent failure: Lost task 0.0 in stage 57.0 (TID 199) (e4cddd0ffcb3 executor driver): java.lang.NullPointerException: Value at index 2 is null\n\tat org.apache.spark.sql.errors.QueryExecutionErrors$.valueIsNullError(QueryExecutionErrors.scala:1733)\n\tat org.apache.spark.sql.Row.getAnyValAs(Row.scala:527)\n\tat org.apache.spark.sql.Row.getFloat(Row.scala:263)\n\tat org.apache.spark.sql.Row.getFloat$(Row.scala:263)\n\tat org.apache.spark.sql.catalyst.expressions.GenericRow.getFloat(rows.scala:166)\n\tat org.apache.spark.ml.recommendation.ALS.$anonfun$fit$2(ALS.scala:713)\n\tat scala.collection.Iterator$$anon$10.next(Iterator.scala:461)\n\tat scala.collection.Iterator$SliceIterator.next(Iterator.scala:273)\n\tat scala.collection.Iterator.foreach(Iterator.scala:943)\n\tat scala.collection.Iterator.foreach$(Iterator.scala:943)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1431)\n\tat scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)\n\tat scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)\n\tat scala.collection.TraversableOnce.to(TraversableOnce.scala:366)\n\tat scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)\n\tat scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)\n\tat scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1431)\n\tat org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1470)\n\tat org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2268)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:136)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)\n\tat java.base/java.lang.Thread.run(Thread.java:833)\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2672)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2608)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2607)\n\tat scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)\n\tat scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2607)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:1182)\n\tat org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:1182)\n\tat scala.Option.foreach(Option.scala:407)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:1182)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2860)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2802)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2791)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:952)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2228)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2249)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2268)\n\tat org.apache.spark.rdd.RDD.$anonfun$take$1(RDD.scala:1470)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:406)\n\tat org.apache.spark.rdd.RDD.take(RDD.scala:1443)\n\tat org.apache.spark.rdd.RDD.$anonfun$isEmpty$1(RDD.scala:1578)\n\tat scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:406)\n\tat org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1578)\n\tat org.apache.spark.ml.recommendation.ALS$.train(ALS.scala:960)\n\tat org.apache.spark.ml.recommendation.ALS.$anonfun$fit$1(ALS.scala:722)\n\tat org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)\n\tat scala.util.Try$.apply(Try.scala:213)\n\tat org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)\n\tat org.apache.spark.ml.recommendation.ALS.fit(ALS.scala:704)\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:77)\n\tat java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.base/java.lang.reflect.Method.invoke(Method.java:568)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.ClientServerConnection.waitForCommands(ClientServerConnection.java:182)\n\tat py4j.ClientServerConnection.run(ClientServerConnection.java:106)\n\tat java.base/java.lang.Thread.run(Thread.java:833)\nCaused by: java.lang.NullPointerException: Value at index 2 is null\n\tat org.apache.spark.sql.errors.QueryExecutionErrors$.valueIsNullError(QueryExecutionErrors.scala:1733)\n\tat org.apache.spark.sql.Row.getAnyValAs(Row.scala:527)\n\tat org.apache.spark.sql.Row.getFloat(Row.scala:263)\n\tat org.apache.spark.sql.Row.getFloat$(Row.scala:263)\n\tat org.apache.spark.sql.catalyst.expressions.GenericRow.getFloat(rows.scala:166)\n\tat org.apache.spark.ml.recommendation.ALS.$anonfun$fit$2(ALS.scala:713)\n\tat scala.collection.Iterator$$anon$10.next(Iterator.scala:461)\n\tat scala.collection.Iterator$SliceIterator.next(Iterator.scala:273)\n\tat scala.collection.Iterator.foreach(Iterator.scala:943)\n\tat scala.collection.Iterator.foreach$(Iterator.scala:943)\n\tat scala.collection.AbstractIterator.foreach(Iterator.scala:1431)\n\tat scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)\n\tat scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)\n\tat scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)\n\tat scala.collection.TraversableOnce.to(TraversableOnce.scala:366)\n\tat scala.collection.TraversableOnce.to$(TraversableOnce.scala:364)\n\tat scala.collection.AbstractIterator.to(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358)\n\tat scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358)\n\tat scala.collection.AbstractIterator.toBuffer(Iterator.scala:1431)\n\tat scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345)\n\tat scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339)\n\tat scala.collection.AbstractIterator.toArray(Iterator.scala:1431)\n\tat org.apache.spark.rdd.RDD.$anonfun$take$2(RDD.scala:1470)\n\tat org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2268)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:136)\n\tat org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:548)\n\tat org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1504)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:551)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1136)\n\tat java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)\n\t... 1 more\n"
     ]
    }
   ],
   "source": [
    "# Train ALS model\n",
    "model = als.fit(training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbb6f5ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train ALS model\n",
    "model = als.fit(training)\n",
    "\n",
    "# Generate top 10 product recommendations for each user\n",
    "userRecs = model.recommendForAllUsers(10)\n",
    "\n",
    "# Mapping back the 'userId' and 'itemId' to original 'user' and 'target' values\n",
    "user_to_string_indexer = IndexToString(inputCol=\"userId\", outputCol=\"user\", labels=index_model_user.labels)\n",
    "item_to_string_indexer = IndexToString(inputCol=\"itemId\", outputCol=\"target\", labels=index_model_target.labels)\n",
    "\n",
    "userRecs = user_to_string_indexer.transform(userRecs)\n",
    "\n",
    "# To convert the recommendations from array format to multiple rows,\n",
    "# we explode the recommendations array\n",
    "userRecs = userRecs.withColumn(\"recommendations\", explode(userRecs.recommendations))\n",
    "\n",
    "userRecs = item_to_string_indexer.transform(userRecs)\n",
    "\n",
    "# Display the recommendations\n",
    "userRecs.select(\"user\", \"recommendations.target\", \"recommendations.rating\").show()\n"
   ]
  }
 ],
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